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Dingel J, Kleine AK, Cecil J, Sigl AL, Lermer E, Gaube S. Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology. J Med Internet Res 2024; 26:e57224. [PMID: 39102675 PMCID: PMC11333871 DOI: 10.2196/57224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. OBJECTIVE This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. METHODS The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. RESULTS The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.
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Affiliation(s)
- Julius Dingel
- Human-AI-Interaction Group, Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany
| | - Anne-Kathrin Kleine
- Human-AI-Interaction Group, Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany
| | - Julia Cecil
- Human-AI-Interaction Group, Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany
| | - Anna Leonie Sigl
- Department of Liberal Arts and Sciences, Technical University of Applied Sciences Augsburg, Augsburg, Germany
| | - Eva Lermer
- Human-AI-Interaction Group, Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany
- Department of Liberal Arts and Sciences, Technical University of Applied Sciences Augsburg, Augsburg, Germany
| | - Susanne Gaube
- Human Factors in Healthcare, Global Business School for Health, University College London, London, United Kingdom
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2
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Wangpitipanit S, Lininger J, Anderson N. Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach. BMC Nurs 2024; 23:529. [PMID: 39090714 PMCID: PMC11295627 DOI: 10.1186/s12912-024-02170-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/11/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing. METHODS We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant's 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources. RESULTS Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare. CONCLUSION This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.
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Affiliation(s)
- Supichaya Wangpitipanit
- Visiting Assistant Professor, Division of Health Informatics, Department of Public Health Sciences, UC Davis School of Medicine, University of California, Davis, USA, Division of Community Health Nursing, Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Jiraporn Lininger
- Division of Community Health Nursing, Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
| | - Nick Anderson
- Division of Health Informatics, Department of Public Health Sciences, UC Davis School of Medicine, University of California, Davis, USA
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3
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Castner N, Arsiwala-Scheppach L, Mertens S, Krois J, Thaqi E, Kasneci E, Wahl S, Schwendicke F. Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study. NPJ Digit Med 2024; 7:199. [PMID: 39068241 PMCID: PMC11283514 DOI: 10.1038/s41746-024-01192-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts' interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI's usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.
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Affiliation(s)
- Nora Castner
- Carl Zeiss Vision International GmbH, Tübingen, Germany.
- University of Tübingen, Tübingen, Germany.
| | | | - Sarah Mertens
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Joachim Krois
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Enkeleda Thaqi
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Enkelejda Kasneci
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Siegfried Wahl
- Carl Zeiss Vision International GmbH, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Falk Schwendicke
- Ludwig Maximilian University, Operative, Preventative and Pediatric Dentistry and Periodontology, Munich, Germany
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4
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Lopes JM, Silva LF, Massano-Cardoso I. AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention. Behav Sci (Basel) 2024; 14:616. [PMID: 39062439 PMCID: PMC11273900 DOI: 10.3390/bs14070616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
The evolution of e-retail and the contribution of artificial intelligence in improving algorithms for greater customer engagement highlight the potential of these technologies to develop e-commerce further, making it more accessible and personalized to meet individual needs. This study aims to explore the psychosocial factors (subjective norms; faith; consciousness; perceived control) that affect AI-enabled ease of use and their impact on purchase intention in online retail. We will also assess the mediating effect of AI-enabled ease of use between psychosocial factors and consumer purchase intention. A quantitative methodology was used, and 1438 responses were collected from Portuguese consumers on e-retail. Structural equation modeling was used for the statistical treatment. The findings indicate that subjective norms do not positively impact AI-enabled ease of use, whereas factors such as faith, consciousness, and perceived control do enhance it. Furthermore, AI-enabled ease of use itself boosts purchase intention. Additionally, the effects of subjective norms, faith, consciousness, and perceived control on purchase intention are significantly enhanced when mediated by AI-enabled ease of use, highlighting the crucial role of usability in shaping consumer purchase behavior. The contribution of this study has been made through the formulation model that provides a systematized perspective about the influencers of purchase intentions and extends the knowledge about the impact of artificial intelligence in e-retail. Furthermore, this study offers insights into the impact of artificial intelligence in e-commerce-artificial intelligence directly affects purchase intentions and plays an important mediator role in the interaction mechanisms between psychosocial factors and purchase intentions.
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Affiliation(s)
- João M. Lopes
- Instituto Superior Miguel Torga, 3000-132 Coimbra, Portugal;
- NECE-UBI—Research Unit in Business Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal
| | - L. Filipe Silva
- Instituto Superior Miguel Torga & Instituto Superior de Contabilidade e Administração, Universidade de Aveiro, 3810-193 Aveiro, Portugal;
| | - Ilda Massano-Cardoso
- Instituto Superior Miguel Torga, 3000-132 Coimbra, Portugal;
- Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
- CEISUC—Faculty of Economics, University of Coimbra, 3004-512 Coimbra, Portugal
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5
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Wichmann J, Gesk TS, Leyer M. Acceptance of AI in Health Care for Short- and Long-Term Treatments: Pilot Development Study of an Integrated Theoretical Model. JMIR Form Res 2024; 8:e48600. [PMID: 39024565 PMCID: PMC11294784 DOI: 10.2196/48600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 02/14/2024] [Accepted: 04/29/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND As digital technologies and especially artificial intelligence (AI) become increasingly important in health care, it is essential to determine whether and why potential users intend to use related health information systems (HIS). Several theories exist, but they focus mainly on aspects of health care or information systems, in addition to general psychological theories, and hence provide a small number of variables to explain future behavior. Thus, research that provides a larger number of variables by combining several theories from health care, information systems, and psychology is necessary. OBJECTIVE This study aims to investigate the intention to use new HIS for decisions concerning short- and long-term medical treatments using an integrated approach with several variables to explain future behavior. METHODS We developed an integrated theoretical model based on theories from health care, information systems, and psychology that allowed us to analyze the duality approach of adaptive and nonadaptive appraisals and their influence on the intention to use HIS. We applied the integrated theoretical model to the short-term treatment using AI-based HIS for surgery and the long-term treatment of diabetes tracking using survey data with structured equation modeling. To differentiate between certain levels of AI involvement, we used several scenarios that include treatments by physicians only, physicians with AI support, and AI only to understand how individuals perceive the influence of AI. RESULTS Our results showed that for short- and long-term treatments, the variables perceived threats, fear (disease), perceived efficacy, attitude (HIS), and perceived norms are important to consider when determining the intention to use AI-based HIS. Furthermore, the results revealed that perceived efficacy and attitude (HIS) are the most important variables to determine intention to use for all treatments and scenarios. In contrast, abilities (HIS) were important for short-term treatments only. For our 9 scenarios, adaptive and nonadaptive appraisals were both important to determine intention to use, depending on whether the treatment is known. Furthermore, we determined R² values that varied between 57.9% and 81.7% for our scenarios, which showed that the explanation power of our model is medium to good. CONCLUSIONS We contribute to HIS literature by highlighting the importance of integrating disease- and technology-related factors and by providing an integrated theoretical model. As such, we show how adaptive and nonadaptive appraisals should be arranged to report on medical decisions in the future, especially in the short and long terms. Physicians and HIS developers can use our insights to identify promising rationale for HIS adoption concerning short- and long-term treatments and adapt and develop HIS accordingly. Specifically, HIS developers should ensure that future HIS act in terms of HIS functions, as our study shows that efficient HIS lead to a positive attitude toward the HIS and ultimately to a higher intention to use.
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Affiliation(s)
- Johannes Wichmann
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
| | - Tanja Sophie Gesk
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
| | - Michael Leyer
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
- Management Department, Queensland University of Technology, Brisbane, Australia
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6
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Song Z, Cai J, Zhou Y, Jiang Y, Huang S, Gu L, Tan J. Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA. J Multidiscip Healthc 2024; 17:3295-3304. [PMID: 39006875 PMCID: PMC11246636 DOI: 10.2147/jmdh.s468539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
Purpose Artificial intelligence (AI) is increasingly influencing various medical fields, including anesthesiology. The Introduction of artificial intelligent patient-controlled analgesia (Ai-PCA) has been seen as a significant advancement in pain management. However, the adoption and practical application of Ai-PCA by medical staff, particularly in anesthesia and thoracic surgery, have not been extensively studied. This study aimed to investigate the knowledge, attitudes and practices (KAP) among anesthesia and thoracic surgery medical staff toward artificial intelligent patient-controlled analgesia (Ai-PCA). Participants and Methods This web-based cross-sectional study was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital. A self-designed questionnaire was developed to collect demographic information of anesthesia and thoracic surgery medical staff, and to assess their knowledge, attitudes and practices toward Ai-PCA. Results A total of 519 valid questionnaires were collected. Among the participants, 278 (53.56%) were female, 497 (95.76%) were employed in the field of anesthesiology, and 188 (36.22%) had participated in Ai-PCA training. The mean knowledge, attitude, and practice scores were 7.8±1.75 (possible range: 0-10), 37.43±4.16 (possible range: 9-45), and 28.38±9.27 (possible range: 9-45), respectively. Conclusion The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA. Comprehensive training programs are needed to improve anesthesia and thoracic surgery medical staff's practices in this area.
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Affiliation(s)
- Zhenghuan Song
- Department of Anesthesiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 21009, People’s Republic of China
| | - Jiaqin Cai
- Xuzhou Medical University, Xuzhou, 21009, People’s Republic of China
| | - Yihu Zhou
- Nanjing Medical University, Nanjing, 21009, People’s Republic of China
| | - Yueyi Jiang
- Nanjing Medical University, Nanjing, 21009, People’s Republic of China
| | - Shiyi Huang
- Xuzhou Medical University, Xuzhou, 21009, People’s Republic of China
| | - Lianbing Gu
- Department of Anesthesiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 21009, People’s Republic of China
- Xuzhou Medical University, Xuzhou, 21009, People’s Republic of China
| | - Jing Tan
- Department of Anesthesiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 21009, People’s Republic of China
- Xuzhou Medical University, Xuzhou, 21009, People’s Republic of China
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7
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Bouhouita-Guermech S, Haidar H. Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context. Asian Bioeth Rev 2024; 16:315-344. [PMID: 39022380 PMCID: PMC11250714 DOI: 10.1007/s41649-024-00292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 07/20/2024] Open
Abstract
The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.
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Affiliation(s)
| | - Hazar Haidar
- Ethics Programs, Department of Letters and Humanities, University of Quebec at Rimouski, Rimouski, Québec Canada
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8
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Tezsezen E, Yigci D, Ahmadpour A, Tasoglu S. AI-Based Metamaterial Design. ACS APPLIED MATERIALS & INTERFACES 2024; 16:29547-29569. [PMID: 38808674 PMCID: PMC11181287 DOI: 10.1021/acsami.4c04486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.
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Affiliation(s)
- Ece Tezsezen
- Graduate
School of Science and Engineering, Koç
University, Istanbul 34450, Türkiye
| | - Defne Yigci
- School
of Medicine, Koç University, Istanbul 34450, Türkiye
| | - Abdollah Ahmadpour
- Department
of Mechanical Engineering, Koç University
Sariyer, Istanbul 34450, Türkiye
| | - Savas Tasoglu
- Department
of Mechanical Engineering, Koç University
Sariyer, Istanbul 34450, Türkiye
- Koç
University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Türkiye
- Bogaziçi
Institute of Biomedical Engineering, Bogaziçi
University, Istanbul 34684, Türkiye
- Koç
University Arçelik Research Center for Creative Industries
(KUAR), Koç University, Istanbul 34450, Türkiye
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9
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El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, Varrassi G, Nagiub MS. Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review. Pain Ther 2024; 13:293-317. [PMID: 38430433 PMCID: PMC11111436 DOI: 10.1007/s40122-024-00584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.
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Affiliation(s)
- Salah N El-Tallawy
- Anesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia.
- Anesthesia and Pain Department, Faculty of Medicine, Minia University & NCI, Cairo University, Giza, Egypt.
| | | | - Ingrid Vasiliu-Feltes
- Science, Entrepreneurship and Investments Institute, University of Miami, Miami, USA
| | - Rania S Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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10
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Frost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform 2024; 186:105417. [PMID: 38564959 DOI: 10.1016/j.ijmedinf.2024.105417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.
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Affiliation(s)
- Emma Kellie Frost
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Rebecca Bosward
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
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11
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Bartenschlager CC, Gassner UM, Römmele C, Brunner JO, Schlögl-Flierl K, Ziethmann P. The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications. Artif Intell Med 2024; 152:102873. [PMID: 38643592 DOI: 10.1016/j.artmed.2024.102873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/27/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.
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Affiliation(s)
- Christina C Bartenschlager
- Nuremberg Technical University of Applied Sciences Georg Simon Ohm, Keßlerplatz 12, 90489, Germany; Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany; Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany.
| | - Ulrich M Gassner
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Faculty of Law, University of Augsburg, Universitätsstraße 24, 86159 Augsburg, Germany; Research Centre for E-Health Law, Faculty of Law, University of Augsburg, Universitätsstraße 24, 86159 Augsburg, Germany
| | - Christoph Römmele
- Internal Medicine III, Gastroenterology and Infectious Diseases, Augsburg University Hospital, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Jens O Brunner
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Department of Technology, Management, and Economics, Technical University of Denmark, Akademivej 358, 127, 2800 Kongens Lyngby, Denmark; Working Group of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Kerstin Schlögl-Flierl
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Faculty of Catholic Theology, University of Augsburg, Universitätsstraße 10, 86159 Augsburg, Germany; German Ethics Council, Jägerstraße 22/23, 10117 Berlin, Germany; Center for Responsible AI Technologies, University of Augsburg, 86135 Augsburg, Germany
| | - Paula Ziethmann
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Center for Responsible AI Technologies, University of Augsburg, 86135 Augsburg, Germany
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12
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Osuna E, Luna A, Pérez-Cárceles MD. [The right to patient autonomy. Updating in daily clinical care]. Med Clin (Barc) 2024; 162:e52-e55. [PMID: 38471981 DOI: 10.1016/j.medcli.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 03/14/2024]
Affiliation(s)
- Eduardo Osuna
- Área de Medicina legal y forense, Instituto de Investigación Biomédica de la Región de Murcia (IMIB), Campus de Excelencia Internacional Campus Mare Nostrum, Universidad de Murcia, Murcia, España
| | - Aurelio Luna
- Área de Medicina legal y forense, Instituto de Investigación Biomédica de la Región de Murcia (IMIB), Campus de Excelencia Internacional Campus Mare Nostrum, Universidad de Murcia, Murcia, España
| | - María D Pérez-Cárceles
- Área de Medicina legal y forense, Instituto de Investigación Biomédica de la Región de Murcia (IMIB), Campus de Excelencia Internacional Campus Mare Nostrum, Universidad de Murcia, Murcia, España.
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13
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Farah L, Borget I, Martelli N, Vallee A. Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review. J Med Internet Res 2024; 26:e51514. [PMID: 38739911 PMCID: PMC11130781 DOI: 10.2196/51514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.
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Affiliation(s)
- Line Farah
- Innovation Center for Medical Devices Department, Foch Hospital, Suresnes, France
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre le Cancer, University Paris-Saclay, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Pharmacy Department, Georges Pompidou European Hospital, Paris, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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Khan SD, Hoodbhoy Z, Raja MHR, Kim JY, Hogg HDJ, Manji AAA, Gulamali F, Hasan A, Shaikh A, Tajuddin S, Khan NS, Patel MR, Balu S, Samad Z, Sendak MP. Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS DIGITAL HEALTH 2024; 3:e0000514. [PMID: 38809946 PMCID: PMC11135672 DOI: 10.1371/journal.pdig.0000514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.
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Affiliation(s)
- Sarim Dawar Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Jee Young Kim
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Henry David Jeffry Hogg
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Afshan Anwar Ali Manji
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Freya Gulamali
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Alifia Hasan
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Asim Shaikh
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Salma Tajuddin
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Nida Saddaf Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Manesh R. Patel
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Zainab Samad
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Mark P. Sendak
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
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15
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Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med 2024; 151:102861. [PMID: 38555850 DOI: 10.1016/j.artmed.2024.102861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University (FIU), Modesto A. Maidique Campus, 11200 S.W. 8th St, RB 261B, Miami, FL 33199, United States.
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Wenderott K, Krups J, Luetkens JA, Weigl M. Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study. APPLIED ERGONOMICS 2024; 117:104243. [PMID: 38306741 DOI: 10.1016/j.apergo.2024.104243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/18/2023] [Accepted: 01/23/2024] [Indexed: 02/04/2024]
Abstract
In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.
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Affiliation(s)
- Katharina Wenderott
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
| | - Jim Krups
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Germany; Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn, Germany
| | - Matthias Weigl
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
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17
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Sacca L, Lobaina D, Burgoa S, Lotharius K, Moothedan E, Gilmore N, Xie J, Mohler R, Scharf G, Knecht M, Kitsantas P. Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review. J Clin Med 2024; 13:2525. [PMID: 38731054 PMCID: PMC11084581 DOI: 10.3390/jcm13092525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.
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Affiliation(s)
- Lea Sacca
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA; (D.L.); (S.B.); (K.L.); (E.M.); (N.G.); (J.X.); (R.M.); (G.S.); (M.K.); (P.K.)
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18
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Zou X, Na Y, Lai K, Liu G. Unpacking public resistance to health Chatbots: a parallel mediation analysis. Front Psychol 2024; 15:1276968. [PMID: 38659671 PMCID: PMC11041026 DOI: 10.3389/fpsyg.2024.1276968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 04/01/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages. Methods Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM). Results Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers. Discussion By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.
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Affiliation(s)
- Xiqian Zou
- School of Journalism and Communication, Tsinghua University, Beijing, China
| | - Yuxiang Na
- School of Journalism and Communication, Jinan University, Guangzhou, Guangdong, China
| | - Kaisheng Lai
- School of Journalism and Communication, Jinan University, Guangzhou, Guangdong, China
| | - Guan Liu
- Center for Computational Communication Studies, Jinan University, Guangzhou, Guangdong, China
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19
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Nong P, Adler-Milstein J, Kardia S, Platt J. Public perspectives on the use of different data types for prediction in healthcare. J Am Med Inform Assoc 2024; 31:893-900. [PMID: 38302616 PMCID: PMC10990535 DOI: 10.1093/jamia/ocae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/02/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
OBJECTIVE Understand public comfort with the use of different data types for predictive models. MATERIALS AND METHODS We analyzed data from a national survey of US adults (n = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level. RESULTS Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal characteristic data (2.36, SD 0.94), and sensitive data (1.88, SD 0.77). Across these categories, we observe a statistically significant positive relationship between trust in health systems' use of patient information and comfort with data use for prediction. DISCUSSION Although public trust is recognized as important for the sustainable expansion of predictive tools, current policy does not reflect public concerns. Low comfort with data use for prediction should be addressed in order to prevent potential negative impacts on trust in healthcare. CONCLUSION Our results provide empirical evidence on public perspectives, which are important for shaping the use of predictive models. Findings demonstrate a need for realignment of policy around the sensitivity of non-clinical data categories.
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Affiliation(s)
- Paige Nong
- Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN 55455, United States
| | - Julia Adler-Milstein
- Division of Clinical Informatics and Digital Transformation, University of California San Francisco Department of Medicine, San Francisco, CA 94143, United States
| | - Sharon Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Jodyn Platt
- Department of Learning Health Sciences, Michigan Medicine, Ann Arbor, MI 48109, United States
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20
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Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
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Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
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21
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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22
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Lysø EH, Hesjedal MB, Skolbekken JA, Solbjør M. Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - A focus group study. Soc Sci Med 2024; 347:116771. [PMID: 38537333 DOI: 10.1016/j.socscimed.2024.116771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) is increasingly used for diagnostic purposes in cancer care. Prostate cancer is one of the most prevalent cancers affecting men worldwide, but current diagnostic approaches have limitations in terms of specificity and sensitivity. Using AI to interpret MR images in prostate cancer diagnostics shows promising results, but raises questions about implementation, user acceptance, trust, and doctor-patient communication. Drawing on approaches from the sociology of expectations and theories about sociotechnical imaginaries, we explore men's expectations of artificial intelligence for prostate cancer diagnostics. We conducted ten focus groups with 48 men aged 54-85 in Norway with various experiences of prostate cancer diagnostics. Five groups of men had been treated for prostate cancer, one group was on active surveillance, two groups had been through prostate cancer diagnostics without having a diagnosis, and two groups of men had no experience with prostate cancer diagnostics or treatment. Data was subject to reflexive thematic analysis. Our analysis suggests that men's expectations of AI for prostate cancer diagnostics come from two perspectives: Technology-centered expectations that build on their conceptions of AI's form and agency, and human-centered expectations of AI that build on their perceptions of patient-professional relationships and decision-making processes. These two perspectives are intertwined in three imaginaries of AI: The tool imaginary, the advanced machine imaginary, and the intelligence imaginary - each carrying distinct expectations and ideas of technologies and humans' role in decision-making processes. These expectations are multifaceted and simultaneously optimistic and pessimistic; while AI is expected to improve the accuracy of cancer diagnoses and facilitate more personalized medicine, AI is also expected to threaten interpersonal and communicational relationships between patients and healthcare professionals, and the maintenance of trust in these relationships. This emphasizes how AI cannot be implemented without caution about maintaining human healthcare relationships.
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Affiliation(s)
- Emilie Hybertsen Lysø
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway.
| | - Maria Bårdsen Hesjedal
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - John-Arne Skolbekken
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - Marit Solbjør
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
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Chadaga K, Prabhu S, Sampathila N, Chadaga R, Bhat D, Sharma AK, Swathi KS. SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques. SLAS Technol 2024; 29:100129. [PMID: 38508237 DOI: 10.1016/j.slast.2024.100129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the "Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the "Liebowitz Social Anxiety Scale questionnaire" and "The fear of speaking in public" are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.
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Affiliation(s)
- Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| | - Rajagopala Chadaga
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Devadas Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Akhilesh Kumar Sharma
- Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - K S Swathi
- Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
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Apoorva S, Nguyen NT, Sreejith KR. Recent developments and future perspectives of microfluidics and smart technologies in wearable devices. LAB ON A CHIP 2024; 24:1833-1866. [PMID: 38476112 DOI: 10.1039/d4lc00089g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.
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Affiliation(s)
- Sasikala Apoorva
- UKF Centre for Advanced Research and Skill Development(UCARS), UKF College of Engineering and Technology, Kollam, Kerala, India, 691 302
| | - Nam-Trung Nguyen
- Queensland Micro and Nanotechnology Centre, Griffith University, 170 Kessels Road, Nathan, 4111, Queensland, Australia.
| | - Kamalalayam Rajan Sreejith
- Queensland Micro and Nanotechnology Centre, Griffith University, 170 Kessels Road, Nathan, 4111, Queensland, Australia.
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25
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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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26
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Denecke K, May R, Rivera-Romero O. Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks. J Med Syst 2024; 48:23. [PMID: 38367119 PMCID: PMC10874304 DOI: 10.1007/s10916-024-02043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/10/2024] [Indexed: 02/19/2024]
Abstract
Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation.
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Affiliation(s)
- Kerstin Denecke
- Institute Patient-centered Digital Health, Bern University of Applied Sciences, Quellgasse 21, Biel, 2502, Switzerland.
| | - Richard May
- Harz University of Applied Sciences, Friedrichstraße 57-59, 38855, Wernigerode, Germany
| | - Octavio Rivera-Romero
- Instituto de Ingeniería Informática (I3US), Universidad de Sevilla, Sevilla, Spain
- Department of Electronic Technology, Universidad de Sevilla, Avda Reina Mercedes s/n, ETSI Informática, G1.43, Sevilla, 41012, Spain
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27
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Barwise AK, Curtis S, Diedrich DA, Pickering BW. Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives. J Am Med Inform Assoc 2024; 31:611-621. [PMID: 38099504 PMCID: PMC10873784 DOI: 10.1093/jamia/ocad224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/14/2023] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVES Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. MATERIALS AND METHODS This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. RESULTS We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. DISCUSSION This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. CONCLUSION Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.
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Affiliation(s)
- Amelia K Barwise
- Biomedical Ethics Research Program, Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55902, United States
| | - Susan Curtis
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN 55902, United States
| | - Daniel A Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55902, United States
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55902, United States
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Gray M, Baird A, Sawyer T, James J, DeBroux T, Bartlett M, Krick J, Umoren R. Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study. JMIR MEDICAL EDUCATION 2024; 10:e50705. [PMID: 38300696 PMCID: PMC10870212 DOI: 10.2196/50705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/18/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Using virtual patients, facilitated by natural language processing, provides a valuable educational experience for learners. Generating a large, varied sample of realistic and appropriate responses for virtual patients is challenging. Artificial intelligence (AI) programs can be a viable source for these responses, but their utility for this purpose has not been explored. OBJECTIVE In this study, we explored the effectiveness of generative AI (ChatGPT) in developing realistic virtual standardized patient dialogues to teach prenatal counseling skills. METHODS ChatGPT was prompted to generate a list of common areas of concern and questions that families expecting preterm delivery at 24 weeks gestation might ask during prenatal counseling. ChatGPT was then prompted to generate 2 role-plays with dialogues between a parent expecting a potential preterm delivery at 24 weeks and their counseling physician using each of the example questions. The prompt was repeated for 2 unique role-plays: one parent was characterized as anxious and the other as having low trust in the medical system. Role-play scripts were exported verbatim and independently reviewed by 2 neonatologists with experience in prenatal counseling, using a scale of 1-5 on realism, appropriateness, and utility for virtual standardized patient responses. RESULTS ChatGPT generated 7 areas of concern, with 35 example questions used to generate role-plays. The 35 role-play transcripts generated 176 unique parent responses (median 5, IQR 4-6, per role-play) with 268 unique sentences. Expert review identified 117 (65%) of the 176 responses as indicating an emotion, either directly or indirectly. Approximately half (98/176, 56%) of the responses had 2 or more sentences, and half (88/176, 50%) included at least 1 question. More than half (104/176, 58%) of the responses from role-played parent characters described a feeling, such as being scared, worried, or concerned. The role-plays of parents with low trust in the medical system generated many unique sentences (n=50). Most of the sentences in the responses were found to be reasonably realistic (214/268, 80%), appropriate for variable prenatal counseling conversation paths (233/268, 87%), and usable without more than a minimal modification in a virtual patient program (169/268, 63%). CONCLUSIONS Generative AI programs, such as ChatGPT, may provide a viable source of training materials to expand virtual patient programs, with careful attention to the concerns and questions of patients and families. Given the potential for unrealistic or inappropriate statements and questions, an expert should review AI chat outputs before deploying them in an educational program.
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Affiliation(s)
- Megan Gray
- Division of Neonatology, University of Washington, Seattle, WA, United States
| | - Austin Baird
- Division of Healthcare Simulation Sciences, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Taylor Sawyer
- Division of Neonatology, University of Washington, Seattle, WA, United States
| | - Jasmine James
- Department of Family Medicine, Providence St Peter, Olympia, WA, United States
| | - Thea DeBroux
- Division of Neonatology, University of Washington, Seattle, WA, United States
| | - Michelle Bartlett
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Jeanne Krick
- Department of Pediatrics, San Antonio Uniformed Services Health Education Consortium, San Antonio, TX, United States
| | - Rachel Umoren
- Division of Neonatology, University of Washington, Seattle, WA, United States
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Adigwe OP, Onavbavba G, Sanyaolu SE. Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Front Artif Intell 2024; 6:1293297. [PMID: 38314120 PMCID: PMC10834749 DOI: 10.3389/frai.2023.1293297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/21/2023] [Indexed: 02/06/2024] Open
Abstract
Background Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. Methods A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Results Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. Conclusion This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.
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Affiliation(s)
- Obi Peter Adigwe
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
| | - Godspower Onavbavba
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
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Marey A, Saad AM, Killeen BD, Gomez C, Tregubova M, Unberath M, Umair M. Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging. BJR Open 2024; 6:tzae018. [PMID: 39086557 PMCID: PMC11290812 DOI: 10.1093/bjro/tzae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/18/2024] [Accepted: 07/10/2024] [Indexed: 08/02/2024] Open
Abstract
Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.
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Affiliation(s)
- Ahmed Marey
- Alexandria University Faculty of Medicine, Alexandria, 21521, Egypt
| | | | | | - Catalina Gomez
- Department of Computer Science, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Mariia Tregubova
- Department of Radiology, Amosov National Institute of Cardiovascular Surgery, Kyiv, 02000, Ukraine
| | - Mathias Unberath
- Department of Computer Science, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Muhammad Umair
- Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins Hospital, Baltimore, MD, 21205, United States
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Wang B, Asan O, Zhang Y. Shaping the future of chronic disease management: Insights into patient needs for AI-based homecare systems. Int J Med Inform 2024; 181:105301. [PMID: 38029700 DOI: 10.1016/j.ijmedinf.2023.105301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/02/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND The rising demand for healthcare resources, especially in chronic disease management, has elevated the importance of Artificial Intelligence (AI) in healthcare. While AI-based homecare systems are being developed, the perspectives of chronic patients, who are one of the primary beneficiaries and risk bearers of these technologies, remain largely under-researched. While recent research has highlighted the importance of AI-based homecare systems, the current understanding of patients' desired designs and features is still limited. OBJECTIVE This paper explores chronic patients' perspectives regarding AI-based homecare systems, an area currently underrepresented in research. We aim to identify the factors influencing their decision to use such systems, elucidate the potential roles of government and other concerned authorities, and provide feedback to AI developers to enhance adoption, system design, and usability and improve the overall healthcare experiences of chronic patients. METHOD A web-based open-ended questionnaire was designed to gather the perspectives of chronic patients about AI-based homecare systems. In total, responses from 181 participants were collected. Using Krippendorff's clustering technique, an inductive thematic analysis was performed to identify the main themes and their respective subthemes. RESULT Through rigorous coding and thematic analysis of the collected responses, we identified four major themes further segmented into thirteen subthemes. These four primary themes were: 1) "Personalized Design", emphasizing the need for patients to manage their health condition better through personalized and educational resources and user-friendly interfaces; 2) "Emotional & Social Support", underscoring the desire for AI systems to facilitate social connectivity and provide emotional support to improve the well-being of chronic patients at home; 3) "System Integration & Proactive Care", addressing the importance of seamless communication, proactive patient monitoring and integration with existing healthcare platforms; and 4) "Ethics & Regulation", prioritizing ethical guidelines, regulatory compliance, and affordability in the design. CONCLUSION This study has offered significant insights into the needs and expectations of chronic patients regarding AI-based home care systems. 'The findings highlight the importance of personalized and accessible care, emotional and social support, seamless system integration, proactive care, and ethical considerations in designing and implementing such systems. By aligning the design and operation of these systems with the lived experiences and expectations of patients, we can better ensure their acceptance and effectiveness.
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Affiliation(s)
- Bijun Wang
- Department of Business Analytics and Data Science, Florida Polytechnic University, Lakeland, FL 33805, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07047, USA.
| | - Yiqi Zhang
- Department of Industrial and Manufacturing Engineering, Penn State University, State College, PA 16801, USA
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32
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Hoebel KV, Bridge CP, Ahmed S, Akintola O, Chung C, Huang RY, Johnson JM, Kim A, Ly KI, Chang K, Patel J, Pinho M, Batchelor TT, Rosen BR, Gerstner ER, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiol Artif Intell 2024; 6:e220231. [PMID: 38197800 PMCID: PMC10831514 DOI: 10.1148/ryai.220231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 01/11/2024]
Abstract
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Katharina V. Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Christopher P. Bridge
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Sara Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Oluwatosin Akintola
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Caroline Chung
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Raymond Y. Huang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jason M. Johnson
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Albert Kim
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - K. Ina Ly
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jay Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Marco Pinho
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Tracy T. Batchelor
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Bruce R. Rosen
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Elizabeth R. Gerstner
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
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Alruwaili MM, Abuadas FH, Alsadi M, Alruwaili AN, Elsayed Ramadan OM, Shaban M, Al Thobaity A, Alkahtani SM, El Arab RA. Exploring nurses' awareness and attitudes toward artificial intelligence: Implications for nursing practice. Digit Health 2024; 10:20552076241271803. [PMID: 39114115 PMCID: PMC11304479 DOI: 10.1177/20552076241271803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
Introduction Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses' awareness and attitudes toward AI-integrated tools used in clinical practice. Methods A descriptive cross-sectional design captured nurses' responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses' AI knowledge, and the general attitudes toward the AI scale. Results Nurses displayed "moderate" levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered "aware" of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration (M = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p < 0.05). Female nurses exhibited a higher proportion of negative attitudes compared to male nurses. Significant differences were also found based on age (χ² = 9.31, p < 0.05), with younger nurses demonstrating more positive attitudes toward AI compared to their older counterparts. Educational background yields significant differences (χ² = 6.70, p < 0.05), with nurses holding undergraduate degrees exhibiting the highest positive attitudes. However, years of nursing experience did not reveal significant variations in attitudes. Conclusion Healthcare and nursing administrators need to work on increasing the nurses' awareness of AI applications and emphasize the importance of integrating such technology into the systems in use. Moreover, addressing nurses' concerns about AI's control and discomfort is crucial, especially considering generational differences, with younger nurses often having more positive attitudes toward technology. Change management strategies may help overcome any hindrances.
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Affiliation(s)
- Majed Mowanes Alruwaili
- Department of Nursing Administration & Education, College of Nursing, Jouf University, Sakaka, Saudi Arabia
| | - Fuad H. Abuadas
- Department of Community Health Nursing, College of Nursing, Jouf University, Sakaka, Saudi Arabia
| | - Mohammad Alsadi
- Department of Nursing Administration & Education, College of Nursing, Jouf University, Sakaka, Saudi Arabia
| | - Abeer Nuwayfi Alruwaili
- Department of Nursing Administration & Education, College of Nursing, Jouf University, Sakaka, Saudi Arabia
| | | | - Mostafa Shaban
- Department of Community Health Nursing, College of Nursing, Jouf University, Sakaka, Saudi Arabia
| | - Abdulellah Al Thobaity
- Department of Medical Surgical Nursing, Nursing College, Taif University, Taif, Saudi Arabia
| | | | - Rabie Adel El Arab
- Department of Health Management and Informatics, AL Moosa College of Health Sciences, Al Ahsa, Saudi Arabia
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Long X, Deng H, Zhang Z, Liu T, Yu X, Gong P, Tian L. Development and evaluation of acceptance scale for artificial intelligence in digestive endoscopy by subjects. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:1844-1853. [PMID: 38448378 PMCID: PMC10930752 DOI: 10.11817/j.issn.1672-7347.2023.230225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Indexed: 03/08/2024]
Abstract
OBJECTIVES Digestive endoscopy is an important diagnostic and therapeutic tool for digestive system diseases. The artificial intelligence (AI)-assisted system in endoscopy (hereinafter referred to as AI in digestive endoscopy) has broad application prospects in the field of digestive endoscopy. The trust and acceptance of endoscopic subjects are the cornerstone of the research, application, and promotion of AI in digestive endoscopy. Currently, the tools for measuring the acceptance of AI in digestive endoscopy by subjects are limited at home and abroad. This study aims to develop a scale for measuring the acceptance of AI in digestive endoscopy by subjects, then to evaluate its reliability and validity. METHODS By conducting literature research, an item pool and dimensions were constructed, and a preliminary scale was constructed using Delphi method. Through the first stage of the survey on the subjects, the reliability and validity of the scale were tested, and the revised scale was used for the second stage of survey on the subjects to further verify the structural validity of the scale. RESULTS The acceptance scale for AI in digestive endoscopy included 11 items in 3 dimensions: accuracy, ethics, benefit and willingness. In the first stage of the survey, 351 valid questionnaires were collected, and the Cronbach's α was 0.864. The correlation coefficient between the total score of the scale and the score of the test item was 0.636, and the Kaiser-Meyer-Olkin (KMO) value in exploratory factor analysis was 0.788. In the second stage of the survey, 335 valid questionnaires were collected, and in confirmatory factor analysis, the χ2/df was 3.774, while the root mean squared error of approximation (RMSEA) was 0.091. CONCLUSIONS Acceptance scale for AI in digestive endoscopy by subjects developed in this study has good reliability and validity.
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Affiliation(s)
- Xiuyan Long
- Department of Pediatrics, Third Xiangya Hospital, Central South University, Changsha 410013.
| | - Haijun Deng
- School of Mathematics & Statistics, Guizhou University of Finance and Economics, Guiyang 550025
| | - Zinan Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050
| | - Tao Liu
- Eight-Years Program of Clinical Medicine, Xiangya School of Medicine, Central South University, Changsha 410013
| | - Xiaoyu Yu
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Pan Gong
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Li Tian
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
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He X, Zheng X, Ding H. Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review. J Med Internet Res 2023; 25:e50342. [PMID: 38109173 PMCID: PMC10758939 DOI: 10.2196/50342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 09/20/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Direct-to-consumer (DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps. OBJECTIVE This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for DTC health care AI apps mentioned in the literature and also provided a reference for future design and development. METHODS This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O'Malley's 5-stage framework. Peer-reviewed papers on DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke's reflective thematic analysis approach. RESULTS Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed. CONCLUSIONS The booming DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.
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Affiliation(s)
- Xin He
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Zheng
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Huiyuan Ding
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
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Cano J, Bertomeu-González V, Fácila L, Hornero F, Alcaraz R, Rieta JJ. Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration. Bioengineering (Basel) 2023; 10:1439. [PMID: 38136030 PMCID: PMC10741001 DOI: 10.3390/bioengineering10121439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.
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Affiliation(s)
- Jesús Cano
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Vicente Bertomeu-González
- Cardiovascular Research Group, Clinical Medicine Department, Miguel Hernández University, 03202 Alicante, Spain;
| | - Lorenzo Fácila
- Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Fernando Hornero
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain;
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
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Townsend BA, Plant KL, Hodge VJ, Ashaolu O, Calinescu R. Medical practitioner perspectives on AI in emergency triage. Front Digit Health 2023; 5:1297073. [PMID: 38125759 PMCID: PMC10731272 DOI: 10.3389/fdgth.2023.1297073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction A proposed Diagnostic AI System for Robot-Assisted Triage ("DAISY") is under development to support Emergency Department ("ED") triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England ("NHS") but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. The objective of this study was to explore NHS health practitioners' perspectives and attitudes towards the future use of AI-supported technologies in ED triage. Methods Between July and August 2022 a qualitative-exploratory research study was conducted to collect and capture the perceptions and attitudes of nine NHS healthcare practitioners to better understand the challenges and benefits of a DAISY deployment. The study was based on a thematic analysis of semi-structured interviews. The study involved qualitative data analysis of the interviewees' responses. Audio-recordings were transcribed verbatim, and notes included into data documents. The transcripts were coded line-by-line, and data were organised into themes and sub-themes. Both inductive and deductive approaches to thematic analysis were used to analyse such data. Results Based on a qualitative analysis of coded interviews with the practitioners, responses were categorised into broad main thematic-types, namely: trust; current practice; social, legal, ethical, and cultural concerns; and empathetic practice. Sub-themes were identified for each main theme. Further quantitative analyses explored the vocabulary and sentiments of the participants when talking generally about NHS ED practices compared to discussing DAISY. Limitations include a small sample size and the requirement that research participants imagine a prototype AI-supported system still under development. The expectation is that such a system would work alongside the practitioner. Findings can be generalisable to other healthcare AI-supported systems and to other domains. Discussion This study highlights the benefits and challenges for an AI-supported triage healthcare solution. The study shows that most NHS ED practitioners interviewed were positive about such adoption. Benefits cited were a reduction in patient wait times in the ED, assistance in the streamlining of the triage process, support in calling for appropriate diagnostics and for further patient examination, and identification of those very unwell and requiring more immediate and urgent attention. Words used to describe the system were that DAISY is a "good idea", "help", helpful, "easier", "value", and "accurate". Our study demonstrates that trust in the system is a significant driver of use and a potential barrier to adoption. Participants emphasised social, legal, ethical, and cultural considerations and barriers to DAISY adoption and the importance of empathy and non-verbal cues in patient interactions. Findings demonstrate how DAISY might support and augment human medical performance in ED care, and provide an understanding of attitudinal barriers and considerations for the development and implementation of future triage AI-supported systems.
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Affiliation(s)
| | - Katherine L. Plant
- Faculty of Engineering & Physical Sciences, University of Southampton, Southampton, Hampshire, United Kingdom
| | - Victoria J. Hodge
- Department of Computer Science, University of York, York, United Kingdom
| | | | - Radu Calinescu
- Department of Computer Science, University of York, York, United Kingdom
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Nagar R, Quirk HD, Anderson PL. User experiences of college students using mental health applications to improve self-care: Implications for improving engagement. Internet Interv 2023; 34:100676. [PMID: 37867616 PMCID: PMC10587513 DOI: 10.1016/j.invent.2023.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/29/2023] [Accepted: 09/27/2023] [Indexed: 10/24/2023] Open
Abstract
The purpose of this study is to evaluate themes on 'user experiences' among college students (N = 265) enrolled in an upper-division Psychopathology course who were assigned a project in which they were instructed to identify a self-care goal, choose from a list of six mental health-focused mobile applications (apps) provided by the instructor, and use the app over the course of three weeks to support progress towards their goal. Prior literature on user experiences typically evaluates user reviews, or asks participants to reflect on past app use or anticipate future use. Students reported their experiences using the app during key decision points: app selection, while using the app, and at the conclusion of the assignment. Using thematic analysis, results identified seven central themes and eight subthemes pertaining to the content of the app (e.g., app features) and the context of using the app (e.g., classroom assignment). Content-wise students liked: 1) features with a strong evidence base, namely, thought diaries and guided meditations; 2) progress tracking, because it increased awareness of mood/stressors, motivated students to see improvement, and helped them stay on track. Students appreciated having 3) crisis support resources; 4) app interfaces that allowed for customization (poor app interfaces were sometimes cited as the reason for disengagement); and 5) apps that included varied, comprehensive resources such that it felt like a one-stop shop. In addition to the content of features and design interface, the context in which mental health apps are introduced and used is important. The remaining themes related to the context in which the app was used, including 6) preparation for app usage, such as reviewing scholarly/credible sources, and 7) social support from fellow students completing the same assignment. Future research should evaluate the 'who, what, when, why, where, and how' of app utilization during key decision points, such as initial app selection or subscription renewal, to better understand the impact of user experience on engagement.
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Affiliation(s)
- Ria Nagar
- Department of Psychology, Georgia State University, United States of America
| | - Heather D. Quirk
- Department of Psychology, Georgia State University, United States of America
| | - Page L. Anderson
- Department of Psychology, Georgia State University, United States of America
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Esmaeilzadeh P. Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey. JMIR Mhealth Uhealth 2023; 11:e46430. [PMID: 38039065 PMCID: PMC10724815 DOI: 10.2196/46430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 10/19/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors. OBJECTIVE This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption. METHODS Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives. RESULTS The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives. CONCLUSIONS The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States
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Vo V, Chen G, Aquino YSJ, Carter SM, Do QN, Woode ME. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Soc Sci Med 2023; 338:116357. [PMID: 37949020 DOI: 10.1016/j.socscimed.2023.116357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/04/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives. METHODOLOGY A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship. RESULTS The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI. CONCLUSIONS While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.
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Affiliation(s)
- Vinh Vo
- Centre for Health Economics, Monash University, Australia.
| | - Gang Chen
- Centre for Health Economics, Monash University, Australia
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Quynh Nga Do
- Department of Economics, Monash University, Australia
| | - Maame Esi Woode
- Centre for Health Economics, Monash University, Australia; Monash Data Futures Research Institute, Australia
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Singh A, Randive S, Breggia A, Ahmad B, Christman R, Amal S. Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists. Cancers (Basel) 2023; 15:5659. [PMID: 38067363 PMCID: PMC10705310 DOI: 10.3390/cancers15235659] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 05/29/2024] Open
Abstract
Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low-moderate perceived workload, suggesting room for improved explanations and data visualization.
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Affiliation(s)
- Akarsh Singh
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (A.S.); (S.R.)
| | - Shruti Randive
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (A.S.); (S.R.)
| | - Anne Breggia
- Maine Health Institute for Research, Scarborough, ME 04074, USA
| | - Bilal Ahmad
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.)
| | | | - Saeed Amal
- The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA
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Rojahn J, Palu A, Skiena S, Jones JJ. American public opinion on artificial intelligence in healthcare. PLoS One 2023; 18:e0294028. [PMID: 37943752 PMCID: PMC10635466 DOI: 10.1371/journal.pone.0294028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 10/15/2023] [Indexed: 11/12/2023] Open
Abstract
Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and "100 years from now." (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.
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Affiliation(s)
- Jessica Rojahn
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
| | - Andrea Palu
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
| | - Steven Skiena
- Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
| | - Jason J. Jones
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York, United States of America
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Wang B, Asan O, Mansouri M. Perspectives of Patients With Chronic Diseases on Future Acceptance of AI-Based Home Care Systems: Cross-Sectional Web-Based Survey Study. JMIR Hum Factors 2023; 10:e49788. [PMID: 37930780 PMCID: PMC10660233 DOI: 10.2196/49788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/18/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients' motivation and intention to adopt such systems. OBJECTIVE This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses. METHODS We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario. RESULTS The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients' attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients' attitudes. CONCLUSIONS This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.
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Affiliation(s)
- Bijun Wang
- Department of Business Analytics and Data Science, Florida Polytechnic University, Lakeland, FL, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institue of Technology, Hoboken, NJ, United States
| | - Mo Mansouri
- School of Systems and Enterprises, Stevens Institue of Technology, Hoboken, NJ, United States
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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Mese I, Taslicay CA, Sivrioglu AK. Improving radiology workflow using ChatGPT and artificial intelligence. Clin Imaging 2023; 103:109993. [PMID: 37812965 DOI: 10.1016/j.clinimag.2023.109993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/19/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023]
Abstract
Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language. Radiology is a vital aspect of modern medicine that involves the use of imaging technologies to diagnose and treat medical conditions artificial intelligence, including ChatGPT, can be integrated into radiology workflows to improve efficiency, accuracy, and patient care. ChatGPT can streamline various radiology workflow steps, including patient registration, scheduling, patient check-in, image acquisition, interpretation, and reporting. While ChatGPT has the potential to transform radiology workflows, there are limitations to the technology that must be addressed, such as the potential for bias in artificial intelligence algorithms and ethical concerns. As technology continues to advance, ChatGPT is likely to become an increasingly important tool in the field of radiology, and in healthcare more broadly.
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Affiliation(s)
- Ismail Mese
- Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, 19 Mayıs, Sinan Ercan Cd. No: 23, Kadıköy/Istanbul 34736, Turkey.
| | | | - Ali Kemal Sivrioglu
- Department of Radiology, Liv Hospital Vadistanbul, Ayazağa Mahallesi, Kemerburgaz Caddesi, Vadistanbul Park Etabı, 7F Blok, 34396 Sarıyer/İstanbul, Turkey
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Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. J Med Internet Res 2023; 25:e48249. [PMID: 37856181 PMCID: PMC10623237 DOI: 10.2196/48249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/07/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of "human intelligence" to AI. OBJECTIVE This study aims to comprehend radiologists' perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. METHODS Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. RESULTS In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. CONCLUSIONS This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.
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Affiliation(s)
- Yanhua Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Ziye Wu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Peicheng Wang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Linbo Xie
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Mengsha Yan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Maoqing Jiang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jianjun Zheng
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jingfeng Zhang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jiming Zhu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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Andrade SM, da Silva-Sauer L, de Carvalho CD, de Araújo ELM, Lima EDO, Fernandes FML, Moreira KLDAF, Camilo ME, Andrade LMMDS, Borges DT, da Silva Filho EM, Lindquist AR, Pegado R, Morya E, Yamauti SY, Alves NT, Fernández-Calvo B, de Souza Neto JMR. Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification. Front Hum Neurosci 2023; 17:1234168. [PMID: 37859768 PMCID: PMC10582524 DOI: 10.3389/fnhum.2023.1234168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
Background Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes. Methods The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into K equally-partitioned subsamples. The model was iterated k times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model. Results A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7. Conclusion These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy. Clinical Trial Registration https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.
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Affiliation(s)
- Suellen Marinho Andrade
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | - Leandro da Silva-Sauer
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | | | - Eloise de Oliveira Lima
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | - Fernanda Maria Lima Fernandes
- Center for Alternative and Renewable Energies (CEAR), Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - Maria Eduarda Camilo
- Laboratory of Ergonomics and Health, Department of Physiotherapy, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - Daniel Tezoni Borges
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | - Ana Raquel Lindquist
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Rodrigo Pegado
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Edgard Morya
- Edmond and Lily Safra International Institute of Neurosciences (IIN-ELS), Macaíba, Rio Grande do Norte, Brazil
| | - Seidi Yonamine Yamauti
- Edmond and Lily Safra International Institute of Neurosciences (IIN-ELS), Macaíba, Rio Grande do Norte, Brazil
| | - Nelson Torro Alves
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
| | - Bernardino Fernández-Calvo
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
- Department of Psychology, Faculty of Educational Sciences and Psychology, University of Cordoba, Córdoba, Spain
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - José Maurício Ramos de Souza Neto
- Center for Alternative and Renewable Energies (CEAR), Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
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Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
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Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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Nong P. Demonstrating Trustworthiness to Patients in Data-Driven Health Care. Hastings Cent Rep 2023; 53 Suppl 2:S69-S75. [PMID: 37963050 DOI: 10.1002/hast.1526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Patient data is used to drive an ecosystem of advanced digital tools in health care, like predictive models or artificial intelligence-based decision support. Patients themselves, however, receive little information about these technologies or how they affect their care. This raises important questions about patient trust and continued engagement in a health care system that extracts their data but does not treat them as key stakeholders. This essay explores these tensions and provides steps forward for health systems as they design advanced health information-technology (IT) policies and practices. It centers patients, their concerns, and the ways they perceive trustworthiness to reframe advanced health IT in service of patient interests.
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Guleria S, Schwartz B, Sharma Y, Fernandes P, Jablonski J, Adewole S, Srivastava S, Rhoads F, Porter M, Yeghyayan M, Hyatt D, Copland A, Ehsan L, Brown D, Syed S. The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning. ARXIV 2023:arXiv:2308.13035v1. [PMID: 37664408 PMCID: PMC10473821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Introduction Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.
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Affiliation(s)
- Shan Guleria
- Rush University Medical Center, Department of Internal Medicine. Chicago, IL 60607
| | - Benjamin Schwartz
- Rush University Medical Center, Department of Internal Medicine. Chicago, IL 60607
| | - Yash Sharma
- University of Virginia, Systems and Information Engineering. Charlottesville, VA 22903
| | - Philip Fernandes
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - James Jablonski
- University of Virginia, Systems and Information Engineering. Charlottesville, VA 22903
| | - Sodiq Adewole
- University of Virginia, Systems and Information Engineering. Charlottesville, VA 22903
| | - Sanjana Srivastava
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Fisher Rhoads
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Michael Porter
- University of Virginia, Systems and Information Engineering. Charlottesville, VA 22903
| | - Michelle Yeghyayan
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Dylan Hyatt
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Andrew Copland
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Lubaina Ehsan
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
| | - Donald Brown
- University of Virginia, Data Science Institute. Charlottesville, VA 22903
| | - Sana Syed
- University of Virginia, Department of Pediatrics. Charlottesville, VA 22903
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