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Weber S, Wyszynski M, Godefroid M, Plattfaut R, Niehaves B. How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey. Comput Struct Biotechnol J 2024; 24:146-159. [PMID: 38434249 PMCID: PMC10904922 DOI: 10.1016/j.csbj.2024.02.009] [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: 11/30/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 03/05/2024] Open
Abstract
To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.
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Affiliation(s)
- Sebastian Weber
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
| | - Marc Wyszynski
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
| | - Marie Godefroid
- University of Siegen, Information Systems, Kohlbettstr. 15, 57072 Siegen, Germany
| | - Ralf Plattfaut
- University of Duisburg-Essen, Information Systems and Transformation Management, Universitätsstr. 9, 45141 Essen, Germany
| | - Bjoern Niehaves
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
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2
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Contaldo MT, Pasceri G, Vignati G, Bracchi L, Triggiani S, Carrafiello G. AI in Radiology: Navigating Medical Responsibility. Diagnostics (Basel) 2024; 14:1506. [PMID: 39061643 PMCID: PMC11276428 DOI: 10.3390/diagnostics14141506] [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: 05/08/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.
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Affiliation(s)
- Maria Teresa Contaldo
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | - Giovanni Pasceri
- Information Society Law Center, Department “Cesare Beccaria”, University of Milan, 20122 Milan, Italy
| | - Giacomo Vignati
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | | | - Sonia Triggiani
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy; (G.V.); (S.T.); (G.C.)
- Radiology and Inverventional Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
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Freeman S, Stewart J, Kaard R, Ouliel E, Goudie A, Dwivedi G, Akhlaghi H. Health consumers' ethical concerns towards artificial intelligence in Australian emergency departments. Emerg Med Australas 2024. [PMID: 38890798 DOI: 10.1111/1742-6723.14449] [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: 12/17/2023] [Revised: 04/10/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs. METHODS Qualitative semi-structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022. RESULTS We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision-making process. There is considerably more approval of AI tools that support clinical decision-making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision-making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI. CONCLUSION Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited.
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Affiliation(s)
- Sam Freeman
- Department of Emergency Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
- Centre for Digital Transformation of Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jonathon Stewart
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
- Cardiovascular Disease and Diabetes Program, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
| | - Rebecca Kaard
- School of Medicine, The University of Notre Dame, Fremantle, Western Australia, Australia
| | - Eden Ouliel
- School of Medicine, The University of Notre Dame, Fremantle, Western Australia, Australia
| | - Adrian Goudie
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
- Department of Emergency Medicine, Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Girish Dwivedi
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
- Cardiovascular Disease and Diabetes Program, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Hamed Akhlaghi
- Department of Emergency Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
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Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Front Digit Health 2024; 6:1352762. [PMID: 38863954 PMCID: PMC11165071 DOI: 10.3389/fdgth.2024.1352762] [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: 12/08/2023] [Accepted: 05/06/2024] [Indexed: 06/13/2024] Open
Abstract
Background Mental health problems are prevalent among people with diabetes, yet often under-diagnosed. Smart sensing, utilizing passively collected digital markers through digital devices, is an innovative diagnostic approach that can support mental health screening and intervention. However, the acceptance of this technology remains unclear. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aimed to investigate (1) the acceptance of smart sensing in a diabetes sample, (2) the determinants of acceptance, and (3) the effectiveness of an acceptance facilitating intervention (AFI). Methods A total of N = 132 participants with diabetes were randomized to an intervention group (IG) or a control group (CG). The IG received a video-based AFI on smart sensing and the CG received an educational video on mindfulness. Acceptance and its potential determinants were assessed through an online questionnaire as a single post-measurement. The self-reported behavioral intention, interest in using a smart sensing application and installation of a smart sensing application were assessed as outcomes. The data were analyzed using latent structural equation modeling and t-tests. Results The acceptance of smart sensing at baseline was average (M = 12.64, SD = 4.24) with 27.8% showing low, 40.3% moderate, and 31.9% high acceptance. Performance expectancy (γ = 0.64, p < 0.001), social influence (γ = 0.23, p = .032) and trust (γ = 0.27, p = .040) were identified as potential determinants of acceptance, explaining 84% of the variance. SEM model fit was acceptable (RMSEA = 0.073, SRMR = 0.059). The intervention did not significantly impact acceptance (γ = 0.25, 95%-CI: -0.16-0.65, p = .233), interest (OR = 0.76, 95% CI: 0.38-1.52, p = .445) or app installation rates (OR = 1.13, 95% CI: 0.47-2.73, p = .777). Discussion The high variance in acceptance supports a need for acceptance facilitating procedures. The analyzed model supported performance expectancy, social influence, and trust as potential determinants of smart sensing acceptance; perceived benefit was the most influential factor towards acceptance. The AFI was not significant. Future research should further explore factors contributing to smart sensing acceptance and address implementation barriers.
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Affiliation(s)
- Johannes Knauer
- Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, Germany
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, Germany
| | - Andreas Schmitt
- Research Institute Diabetes Academy Mergentheim (FIDAM), Bad Mergentheim, Germany
| | - Yannik Terhorst
- Department of Psychological Methods and Assessment, Ludwigs-Maximilian University Munich, Munich, Germany
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Newlands R, Bruhn H, Díaz MR, Lip G, Anderson LA, Ramsay C. A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide. BMC Health Serv Res 2024; 24:569. [PMID: 38698386 PMCID: PMC11067265 DOI: 10.1186/s12913-024-10926-z] [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: 02/16/2024] [Accepted: 03/28/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing 'important' stakeholders. METHODS A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: (A) Identify possible stakeholders (B) Explore stakeholders' perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage 'important' stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership. RESULTS Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of 'important' stakeholders. CONCLUSIONS Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform. STUDY REGISTRATION researchregistry6579, date of registration: 16/02/2021.
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Affiliation(s)
- Rumana Newlands
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK.
| | - Hanne Bruhn
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | | | - Gerald Lip
- North East Scotland Breast Screening Programme, NHS Grampian, Aberdeen, UK
| | - Lesley A Anderson
- Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Craig Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
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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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7
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Kerstan S, Bienefeld N, Grote G. Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:939-957. [PMID: 37722964 DOI: 10.1111/risa.14216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 07/05/2023] [Accepted: 07/08/2023] [Indexed: 09/20/2023]
Abstract
The development of artificial intelligence (AI) in healthcare is accelerating rapidly. Beyond the urge for technological optimization, public perceptions and preferences regarding the application of such technologies remain poorly understood. Risk and benefit perceptions of novel technologies are key drivers for successful implementation. Therefore, it is crucial to understand the factors that condition these perceptions. In this study, we draw on the risk perception and human-AI interaction literature to examine how explicit (i.e., deliberate) and implicit (i.e., automatic) comparative trust associations with AI versus physicians, and knowledge about AI, relate to likelihood perceptions of risks and benefits of AI in healthcare and preferences for the integration of AI in healthcare. We use survey data (N = 378) to specify a path model. Results reveal that the path for implicit comparative trust associations on relative preferences for AI over physicians is only significant through risk, but not through benefit perceptions. This finding is reversed for AI knowledge. Explicit comparative trust associations relate to AI preference through risk and benefit perceptions. These findings indicate that risk perceptions of AI in healthcare might be driven more strongly by affect-laden factors than benefit perceptions, which in turn might depend more on reflective cognition. Implications of our findings and directions for future research are discussed considering the conceptualization of trust as heuristic and dual-process theories of judgment and decision-making. Regarding the design and implementation of AI-based healthcare technologies, our findings suggest that a holistic integration of public viewpoints is warranted.
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Affiliation(s)
- Sophie Kerstan
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Nadine Bienefeld
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Gudela Grote
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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8
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Lu L, Zhong Y, Luo S, Liu S, Xiao Z, Ding J, Shao J, Fu H, Xu J. Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications. Health Informatics J 2024; 30:14604582241262961. [PMID: 38881290 DOI: 10.1177/14604582241262961] [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: 06/18/2024]
Abstract
Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.
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Affiliation(s)
- Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Yun Zhong
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Shuqing Luo
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Sichen Liu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Zhongzhou Xiao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jinru Ding
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jin Shao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Hailong Fu
- Department of Anesthesiology, Changzheng Hospital, Naval Medical University, Shanghai, P.R. china
| | - Jie Xu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- Université de Montpellier, Montpellier, France
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Huang Y, Cheung CY, Li D, Tham YC, Sheng B, Cheng CY, Wang YX, Wong TY. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Lond) 2024; 38:464-472. [PMID: 37709926 PMCID: PMC10858189 DOI: 10.1038/s41433-023-02724-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/26/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.
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Affiliation(s)
- Yu Huang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ching Yu Cheng
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.
- Tsinghua Medicine, Tsinghua University, Beijing, China.
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
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10
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Sassi Z, Hahn M, Eickmann S, Herrmann-Johns A, Tretter M. Beyond algorithmic trust: interpersonal aspects on consent delegation to LLMs. JOURNAL OF MEDICAL ETHICS 2024; 50:139. [PMID: 38176901 DOI: 10.1136/jme-2023-109799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Zeineb Sassi
- Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany
| | - Michael Hahn
- Theology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sascha Eickmann
- Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany
| | - Anne Herrmann-Johns
- Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
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11
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Dlugatch R, Georgieva A, Kerasidou A. AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians' and midwives' perspectives on integrating AI-driven CTG into clinical decision making. BMC Med Ethics 2024; 25:6. [PMID: 38184595 PMCID: PMC10771643 DOI: 10.1186/s12910-023-00990-1] [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: 02/03/2023] [Accepted: 11/24/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. METHODS This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. RESULTS There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. CONCLUSIONS Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice.
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Affiliation(s)
- Rachel Dlugatch
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Usher Institute, Old Medical School, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Antoniya Georgieva
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3 Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Angeliki Kerasidou
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK.
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12
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Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel) 2024; 12:125. [PMID: 38255014 PMCID: PMC10815906 DOI: 10.3390/healthcare12020125] [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: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.
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Affiliation(s)
- Molly Bekbolatova
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Jonathan Mayer
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Chi Wei Ong
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Milan Toma
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
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Park HJ. Patient perspectives on informed consent for medical AI: A web-based experiment. Digit Health 2024; 10:20552076241247938. [PMID: 38698829 PMCID: PMC11064747 DOI: 10.1177/20552076241247938] [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: 12/15/2023] [Accepted: 03/28/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Despite the increasing use of AI applications as a clinical decision support tool in healthcare, patients are often unaware of their use in the physician's decision-making process. This study aims to determine whether doctors should disclose the use of AI tools in diagnosis and what kind of information should be provided. Methods A survey experiment with 1000 respondents in South Korea was conducted to estimate the patients' perceived importance of information regarding the use of an AI tool in diagnosis in deciding whether to receive the treatment. Results The study found that the use of an AI tool increases the perceived importance of information related to its use, compared with when a physician consults with a human radiologist. Information regarding the AI tool when AI is used was perceived by participants either as more important than or similar to the regularly disclosed information regarding short-term effects when AI is not used. Further analysis revealed that gender, age, and income have a statistically significant effect on the perceived importance of every piece of AI information. Conclusions This study supports the disclosure of AI use in diagnosis during the informed consent process. However, the disclosure should be tailored to the individual patient's needs, as patient preferences for information regarding AI use vary across gender, age and income levels. It is recommended that ethical guidelines be developed for informed consent when using AI in diagnoses that go beyond mere legal requirements.
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Affiliation(s)
- Hai Jin Park
- Center for AI and Law, Hanyang University Law School, Seoul, South Korea
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14
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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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15
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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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Rodler S, Kopliku R, Ulrich D, Kaltenhauser A, Casuscelli J, Eismann L, Waidelich R, Buchner A, Butz A, Cacciamani GE, Stief CG, Westhofen T. Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial. Eur Urol Focus 2023:S2405-4569(23)00237-7. [PMID: 37923632 DOI: 10.1016/j.euf.2023.10.020] [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: 09/04/2023] [Revised: 10/04/2023] [Accepted: 10/21/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear. OBJECTIVE To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients. DESIGN, SETTING, AND PARTICIPANTS A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC). INTERVENTION Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists. RESULTS AND LIMITATIONS Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t924 = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t921 = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t925 = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario. CONCLUSIONS Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities. PATIENT SUMMARY Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.
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Affiliation(s)
- Severin Rodler
- Department of Urology, LMU University Hospital, Munich, Germany; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Rega Kopliku
- Department of Urology, LMU University Hospital, Munich, Germany
| | - Daniel Ulrich
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | - Annika Kaltenhauser
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | | | - Lennert Eismann
- Department of Urology, LMU University Hospital, Munich, Germany
| | | | | | - Andreas Butz
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | - Giovanni E Cacciamani
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Thilo Westhofen
- Department of Urology, LMU University Hospital, Munich, Germany
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Wagner W, Viidalepp A, Idoiaga-Mondragon N, Talves K, Lillemäe E, Pekarev J, Otsus M. Lay representations of artificial intelligence and autonomous military machines. PUBLIC UNDERSTANDING OF SCIENCE (BRISTOL, ENGLAND) 2023; 32:926-943. [PMID: 37194940 DOI: 10.1177/09636625231167071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This study is about how lay persons perceive and represent artificial intelligence in general as well as its use in weaponised autonomous ground vehicles in the military context. We analysed the discourse of six focus groups in Estonia, using an automatic text analysis tool and complemented the results by a qualitative thematic content analysis. The findings show that representations of artificial intelligence-driven machines are anchored in the image of man. A cluster analysis revealed five dominant themes: artificial intelligence as programmed machines, artificial intelligence and the problem of control, artificial intelligence and its relation to human life, artificial intelligence used in wars and ethical problems in developing autonomous weaponised machines. The findings are discussed with regard to people's tendency to anthropomorphise robots despite their lack of emotions, which can be seen as a last resort when confronting an autonomous machine where the usual interpersonal understanding of intentions does not apply.
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Akinrinmade AO, Adebile TM, Ezuma-Ebong C, Bolaji K, Ajufo A, Adigun AO, Mohammad M, Dike JC, Okobi OE. Artificial Intelligence in Healthcare: Perception and Reality. Cureus 2023; 15:e45594. [PMID: 37868407 PMCID: PMC10587915 DOI: 10.7759/cureus.45594] [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: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.
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Affiliation(s)
- Abidemi O Akinrinmade
- Medicine and Surgery, Benjamin S. Carson School of Medicine, Babcock University, Ilishan-Remo, NGA
| | - Temitayo M Adebile
- Public Health, Georgia Southern University, Statesboro, USA
- Nephrology, Boston Medical Center, Malden, USA
| | | | | | | | - Aisha O Adigun
- Infectious Diseases, University of Louisville, Louisville, USA
| | - Majed Mohammad
- Geriatrics, Mount Carmel Grove City Hospital, Grove City, USA
| | - Juliet C Dike
- Internal Medicine, University of Calabar, Calabar, NGA
| | - Okelue E Okobi
- Family Medicine, Larkin Community Hospital Palm Springs Campus, Miami, USA
- Family Medicine, Medficient Health Systems, Laurel, USA
- Family Medicine, Lakeside Medical Center, Belle Glade, USA
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19
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Giannouli V. Financial capacity assessments and AI: A Greek drama for geriatric psychiatry? Int J Geriatr Psychiatry 2023; 38:e6008. [PMID: 37724603 DOI: 10.1002/gps.6008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Affiliation(s)
- Vaitsa Giannouli
- School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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Ibba S, Tancredi C, Fantesini A, Cellina M, Presta R, Montanari R, Papa S, Alì M. How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders. Eur J Radiol 2023; 165:110917. [PMID: 37327548 DOI: 10.1016/j.ejrad.2023.110917] [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: 02/13/2023] [Revised: 05/16/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey. METHOD We created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:Only completed questionnaires were considered for analysis. RESULTS 2119 subjects completed the survey. Among them, 1216 respondents were over 60 years old, showing interest in AI even though they were not digital natives. Although >45% of the respondents reported a high level of education, only 3% said they were AI experts. 87% of respondents favored using AI to support diagnosis but would like to be informed. Only 10% would consult another specialist if their doctor used AI support. Most respondents (76%) said they would not feel comfortable if the diagnosis was made by the AI alone, highlighting the importance of the physician's role in the emotional management of the patient. Finally, 36% of respondents were willing to discuss the topic further in a focus group. CONCLUSION Patients' perception of the use of AI in radiology was positive, although still strictly linked to the supervision of the radiologist. Respondents showed interest and willingness to learn more about AI in the medical field, confirming how patients' confidence in AI technology and its acceptance is central to its widespread use in clinical practice.
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Affiliation(s)
- Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Chiara Tancredi
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Arianna Fantesini
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Roberta Presta
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging S.p.A., Via Egidio Folli, 50, 20134 Milan, Italy.
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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23
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Visram S, Leyden D, Annesley O, Bappa D, Sebire NJ. Engaging children and young people on the potential role of artificial intelligence in medicine. Pediatr Res 2023; 93:440-444. [PMID: 35393524 PMCID: PMC9937917 DOI: 10.1038/s41390-022-02053-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/15/2022] [Accepted: 03/21/2022] [Indexed: 11/08/2022]
Abstract
INTRODUCTION There is increasing interest in Artificial Intelligence (AI) and its application to medicine. Perceptions of AI are less well-known, notably amongst children and young people (CYP). This workshop investigates attitudes towards AI and its future applications in medicine and healthcare at a specialised paediatric hospital using practical design scenarios. METHOD Twenty-one members of a Young Persons Advisory Group for research contributed to an engagement workshop to ascertain potential opportunities, apprehensions, and priorities. RESULTS When presented as a selection of practical design scenarios, we found that CYP were more open to some applications of AI in healthcare than others. Human-centeredness, governance and trust emerged as early themes, with empathy and safety considered as important when introducing AI to healthcare. Educational workshops with practical examples using AI to help, but not replace humans were suggested to address issues, build trust, and effectively communicate about AI. CONCLUSION Whilst policy guidelines acknowledge the need to include children and young people to develop AI, this requires an enabling environment for human-centred AI involving children and young people with lived experiences of healthcare. Future research should focus on building consensus on enablers for an intelligent healthcare system designed for the next generation, which fundamentally, allows co-creation. IMPACT Children and young people (CYP) want to be included to share their insights about the development of research on the potential role of Artificial Intelligence (AI) in medicine and healthcare and are more open to some applications of AI than others. Whilst it is acknowledged that a research gap on involving and engaging CYP in developing AI policies exists, there is little in the way of pragmatic and practical guidance for healthcare staff on this topic. This requires research on enabling environments for ongoing digital cooperation to identify and prioritise unmet needs in the application and development of AI.
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Affiliation(s)
- Sheena Visram
- Department of Computer Science | UCL Interaction Centre, University College London, London, UK.
- DRIVE Centre, Great Ormond Street Hospital for Children, London, UK.
| | - Deirdre Leyden
- Young Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, UK
| | - Oceiah Annesley
- Young Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, UK
| | - Dauda Bappa
- Young Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, UK
| | - Neil J Sebire
- DRIVE Centre, Great Ormond Street Hospital for Children, London, UK
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Gupta P, Marigi EM, Sanchez-Sotelo J. Research on artificial intelligence in shoulder and elbow surgery is increasing. JSES Int 2022; 7:158-161. [PMID: 36820427 PMCID: PMC9937849 DOI: 10.1016/j.jseint.2022.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Background Total health care spending in the United States is increasing. In order to improve our delivery of high-quality, patient-centric, and cost-effective care, artificial intelligence (AI) and its subsets are being increasingly explored and utilized in medicine. Applications of AI in orthopedic surgery, including shoulder and elbow surgery, are being actively studied and have stirred much discussion. However, the trends of research on AI applications in shoulder and elbow surgery have not yet been quantified. Thus, the purpose of this study is to explore the general trends of research in applying AI to shoulder and elbow surgery and to examine characteristics of these research publications. Methods A literature search was conducted using PubMed for all articles published between January 1, 2000 and May 12, 2022. The primary search query used was as follows: (shoulder) and (AI OR machine learning OR deep learning OR neural networks). Exclusion criteria were as follows: (1) not pertinent to orthopedic surgeons (2) not pertaining to shoulder or elbow surgery, and (3) not pertaining to AI, machine learning, and deep learning. Selected articles in high-impact and relevant orthopedic journals were further characterized and analyzed. Results The annual number of articles increased from 1 article in 2006 to 24 articles in 2021. There was a 4-fold increase in publications between 2019 and 2021, and a 6-fold increase between 2018 and 2021. The average number of publications per year increased exponentially from 2010 to 2021 (R2 = 0.608; P = .003). The three journals with the most publications were Journal of Shoulder and Elbow Surgery (12), followed by Arthroscopy (2), and Clinical Orthopaedics and Related Research (2). Conclusion This study provides quantitative evidence for the first time that publications applying AI and its subsets to shoulder and elbow surgery are growing exponentially since the year 2010, with the most rapid growth beginning between the years of 2019 and 2020.
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Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Erick M. Marigi
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Joaquin Sanchez-Sotelo
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA,Corresponding author: Joaquin Sanchez-Sotelo, MD, PhD, Department of Orthopaedic Surgery, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA.
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Yigitcanlar T, Degirmenci K, Inkinen T. Drivers behind the public perception of artificial intelligence: insights from major Australian cities. AI & SOCIETY 2022:1-21. [PMID: 36212229 PMCID: PMC9527736 DOI: 10.1007/s00146-022-01566-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 09/14/2022] [Indexed: 10/27/2022]
Abstract
Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness.
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Affiliation(s)
- Tan Yigitcanlar
- City 4.0 Lab, School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia
| | - Kenan Degirmenci
- School of Information Systems, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia
| | - Tommi Inkinen
- Department of Geography and Geology, University of Turku, Turun Yliopisto, 20014 Turku, Finland
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Ngo B, Nguyen D, vanSonnenberg E. The Cases for and against Artificial Intelligence in the Medical School Curriculum. Radiol Artif Intell 2022; 4:e220074. [PMID: 36204540 PMCID: PMC9530767 DOI: 10.1148/ryai.220074] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/26/2022] [Accepted: 08/02/2022] [Indexed: 06/02/2023]
Abstract
Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum. AI likely will affect all medical specialties, perhaps radiology more so than any other. The purpose of this article is to present a balanced perspective on whether AI should be included officially in the medical school curriculum. After presenting the balanced point-counterpoint arguments, the authors provide a compromise. Keywords: Artificial Intelligence, Medical Education, Medical School Curriculum, Medical Students, Radiology, Use of AI in Education © RSNA, 2022.
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Wong DYL, Lam MC, Ran A, Cheung CY. Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Curr Opin Ophthalmol 2022; 33:440-446. [PMID: 35916571 DOI: 10.1097/icu.0000000000000886] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.
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Affiliation(s)
- Dragon Y L Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
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Andeobu L, Wibowo S, Grandhi S. Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155389. [PMID: 35460765 DOI: 10.1016/j.scitotenv.2022.155389] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 05/17/2023]
Abstract
Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.
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Affiliation(s)
- Lynda Andeobu
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
| | - Santoso Wibowo
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
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Brons A, Braam K, Broekema A, Timmerman A, Millenaar K, Engelbert R, Kröse B, Visser B. Translating Promoting Factors and Behavior Change Principles Into a Blended and Technology-Supported Intervention to Stimulate Physical Activity in Children With Asthma (Foxfit): Design Study. JMIR Form Res 2022; 6:e34121. [PMID: 35877162 PMCID: PMC9361148 DOI: 10.2196/34121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 11/28/2022] Open
Abstract
Background Children with asthma can decrease the impact of their disease by improving their physical activity (PA). However, health care providers lack interventions for children with asthma that effectively increase their PA levels and achieve behavior change. A technology-supported approach can positively influence PA and physical functioning in children. Objective The aims of this study were to develop a technology-supported intervention that facilitates health care providers in promoting PA for children (aged 8 to 12 years) with asthma and to systematically describe this developmental process. Methods Intervention mapping (IM) was applied to develop a blended and technology-supported intervention in cocreation with children with asthma, their parents, and health care providers. In accordance with the IM framework, the following steps were performed: conduct a needs assessment; define the intervention outcome, performance objectives, and change objectives; select theory-based intervention methods and strategies; create components of the intervention and conduct pilot tests; create an implementation plan; and create an evaluation plan. Results We developed the blended intervention Foxfit that consists of an app with a PA monitor for children (aged 8 to 12 years) with asthma and a web-based dashboard for their health care provider. The intervention focuses on PA in everyday life to improve social participation. Foxfit contains components based on behavior change principles and gamification, including goal setting, rewards, action planning, monitoring, shaping knowledge, a gamified story, personal coaching and feedback, and a tailored approach. An evaluation plan was created to assess the intervention’s usability and feasibility for both children and health care providers. Conclusions The IM framework was very useful for systematically developing a technology-supported intervention and for describing the translational process from scientific evidence, the needs and wishes of future users, and behavior change principles into this intervention. This has led to the technology-supported intervention Foxfit that facilitates health care providers in promoting PA in children with asthma. The structured description of the development process and functional components shows the way behavior change techniques are incorporated in the intervention. Trial Registration International Clinical Trial Registry Platform NTR6658; https://tinyurl.com/3rxejksf
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Affiliation(s)
- Annette Brons
- Digital Life Center, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Katja Braam
- Centre of Expertise Urban Vitality, University of Applied Sciences Amsterdam, Amsterdam, Netherlands
- Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Haarlem, Netherlands
| | - Aline Broekema
- Centre of Expertise Urban Vitality, University of Applied Sciences Amsterdam, Amsterdam, Netherlands
| | | | - Karel Millenaar
- Play and Civid Media, University of Applied Sciences Amsterdam, Amsterdam, Netherlands
| | - Raoul Engelbert
- Centre of Expertise Urban Vitality, University of Applied Sciences Amsterdam, Amsterdam, Netherlands
- Department of Rehabilitation, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Ben Kröse
- Digital Life Center, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
- Department of Computer Science, University of Amsterdam, Amsterdam, Netherlands
| | - Bart Visser
- Centre of Expertise Urban Vitality, University of Applied Sciences Amsterdam, Amsterdam, Netherlands
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Singhal A, Baxi MK, Mago V. Synergy between Public and Private Healthcare Organizations during COVID-19 on Twitter. JMIR Med Inform 2022; 10:e37829. [PMID: 35849795 PMCID: PMC9390834 DOI: 10.2196/37829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/08/2022] [Accepted: 07/15/2022] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and NGOs for communicating health concerns, new advancements, and potential outbreaks. While the benefits of using them as a tool have been extensively discussed, the online activity of various healthcare organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated. OBJECTIVE The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations. METHODS Data was collected from the Twitter handles of five pharmaceutical companies, ten U.S. and Canadian public health agencies, and World Health Organization (WHO) between January 01, 2017 - December 31, 2021. A total of 181,469 tweets were divided into two phases for the analysis: before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using Natural Language Processing (NLP) based topic modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents. RESULTS We utilized the topics modeled from the tweets authored by the health organizations chosen for our analysis using Non-Negative Matrix Factorization (NMF) ('c_umass' scores: -3.6530 and -3.7944, before COVID-19 and during COVID-19 respectively). The topics are - 'Chronic Diseases', 'Health Research', 'Community Healthcare', 'Medical Trials', 'COVID-19', 'Vaccination', 'Nutrition and Well-being', and 'Mental Health'. In terms of user impact, WHO (user impact: 4171.24) had the highest impact overall, followed by the public health agencies, CDC (user impact: 2895.87), and NIH (user impact: 891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, ARIMA and SARIMAX models performed best on the majority of the subsets of data (divided as per the health organization and time-period), with Mean Absolute Error (MAE) between 0.027 - 0.084, Mean Squared Error (MSE) between 0.001 - 0.011, and Root Mean Squared Error (RMSE) between 0.031 - 0.105. CONCLUSIONS Our findings indicate that people engage more on topics like 'COVID-19' than 'Medical Trials', 'Customer Experience'. Also, there are notable differences in the user engagement levels across organizations. Global organizations, like WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement. CLINICALTRIAL
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Affiliation(s)
- Aditya Singhal
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, CA
| | - Manmeet Kaur Baxi
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, CA
| | - Vijay Mago
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, CA
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Kuznetsova YM. Emotional Attitudes towards the Components of the Digital Environment (Based on the Text Analysis of Network Comments). RUDN JOURNAL OF PSYCHOLOGY AND PEDAGOGICS 2022. [DOI: 10.22363/2313-1683-2022-19-2-253-281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
One of the psychological effects of digitalization is the establishment of specific relationships between a person and the cyber environment and its components. The paper presents the results of a study of the emotional component of attitudes towards the components of the digital environment, carried out using emotive-predicate analysis, a new method of computer text processing implemented in TITANIS, an advanced social media text analysis tool. Using this method, it is possible to automatically extract descriptions of emotional situations from texts in which the components of the digital environment are the cause or subject of 68 emotional states. The texts of 2048 online videos posted in the Russian-language segment of YouTube served as the material for the analysis. According to the frequency of occurrence, emotional situations involving various components of the digital environment are quite typical for even thematically irrelevant online discussions. The components of the digital environment mentioned in non-thematic discussions as participants in emotional situations are classified into the following three groups: (1) general concepts of digital technologies; (2) digital devices; (3) activities mediated by digital technologies. The lexemes of the latter group, denoting various aspects of network communication, are included in the vast majority of descriptions of emotional situations involving the components of the digital environment, and six times more often as causes of emotions than as subjects of emotional states. In general, the emotional attitude towards the components of the cyber environment as a whole is characterized as balanced, without a noticeable predominance of negative or positive emotions. However, negative states are more often attributed to the components of the cyber environment as the subjects than as the causes of emotions. The practical significance of the described method of text analysis as a means of assessing the emotional component of attitudes towards the components of the digital environment is determined by the influence that affective reactions of users have on the demand for technical innovations and the direction of their development.
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Rakovic K, Colling R, Browning L, Dolton M, Horton MR, Protheroe A, Lamb AD, Bryant RJ, Scheffer R, Crofts J, Stanislaus E, Verrill C. The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters. Diagnostics (Basel) 2022; 12:1225. [PMID: 35626380 PMCID: PMC9141178 DOI: 10.3390/diagnostics12051225] [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: 04/11/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 12/03/2022] Open
Abstract
There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (<1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed.
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Affiliation(s)
- Kai Rakovic
- Institute of Cancer Sciences, University of Glasgow, Switchback Road, Glasgow G61 1QH, UK
- Department of Pathology, Queen Elizabeth University Hospital, Govan Road, Glasgow G51 4TF, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (R.C.); (L.B.); (C.V.)
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (R.C.); (L.B.); (C.V.)
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Monica Dolton
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
| | | | - Andrew Protheroe
- Department of Oncology, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, UK;
- Oxford Cancer & Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford OX3 7LE, UK
| | - Alastair D. Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford OX3 7LE, UK
| | - Richard J. Bryant
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford OX3 7LE, UK
| | - Richard Scheffer
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
| | - James Crofts
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
| | - Ewart Stanislaus
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (R.C.); (L.B.); (C.V.)
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK; (M.D.); (A.D.L.); (R.J.B.); (R.S.); (J.C.); (E.S.)
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
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Yap A, Wilkinson B, Chen E, Han L, Vaghefi E, Galloway C, Squirrell D. Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening. Asia Pac J Ophthalmol (Phila) 2022; 11:287-293. [PMID: 35772087 DOI: 10.1097/apo.0000000000000525] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
PURPOSE Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening. DESIGN Survey. METHODS Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems. RESULTS The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy. CONCLUSIONS There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.
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Affiliation(s)
- Aaron Yap
- Department of Ophthalmology, Auckland, New Zealand
| | - Benjamin Wilkinson
- Department of Ophthalmology, University of Auckland, Auckland, New Zealand
| | - Eileen Chen
- School of Optometry and Vision Science, Auckland, New Zealand
| | - Lydia Han
- School of Optometry and Vision Science, Auckland, New Zealand
| | - Ehsan Vaghefi
- School of Optometry and Vision Science, Auckland, New Zealand
- Toku Eyes, Auckland, New Zealand
| | - Chris Galloway
- School of Communication, Journalism and Marketing Massey Business School, New Zealand
| | - David Squirrell
- Department of Ophthalmology, Auckland, New Zealand
- Toku Eyes, Auckland, New Zealand
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Çitil ET, Çitil Canbay F. Artificial intelligence and the future of midwifery: What do midwives think about artificial intelligence? A qualitative study. Health Care Women Int 2022; 43:1510-1527. [PMID: 35452353 DOI: 10.1080/07399332.2022.2055760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The evidence on how AI will make a revolution is insufficient. Our aim was to investigate opinions of midwives on the future of AI and midwifery. Semi-structured interviews were done with 18 midwives in Turkey. Themes were identified: expectations included the advantages and conditional acceptance of robotic technology, prejudices reflected perceived shortcomings, lack of human competencies, and trust issues. Concerns included midwifery care and concerns about her future. Midwives were overwhelmingly skeptical about the replacement of human capabilities by AI and found the technology's potential limited.
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Affiliation(s)
- Elif Tuğçe Çitil
- Department of Midwifery, Health Science Faculty, Kütahya Health Science University, Kütahya, Turkey
| | - Funda Çitil Canbay
- Department of Midwifery, Health Science Faculty, Atatürk University, Erzurum, Turkey
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Nader K, Toprac P, Scott S, Baker S. Public understanding of artificial intelligence through entertainment media. AI & SOCIETY 2022:1-14. [PMID: 35400854 PMCID: PMC8976224 DOI: 10.1007/s00146-022-01427-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/01/2022] [Indexed: 11/27/2022]
Abstract
Artificial intelligence (AI) is becoming part of our everyday experience and is expected to be ever more integrated into ordinary life for many years to come. Thus, it is important for those in product development, research, and public policy to understand how the public's perception of AI is shaped. In this study, we conducted focus groups and an online survey to determine the knowledge of AI held by the American public, and to judge whether entertainment media is a major influence on how Americans perceive AI. What we found is that the American public's knowledge of AI is patchy: some have a good understanding of what is and what is not AI, but many do not. When it came to understanding what AI can do, most respondents believe that AI could "replace human jobs" but few thought that it could "feel emotion." Most respondents were optimistic about the future and impact of AI, though about one third were not sure. Most respondents also did not think they could develop an emotional bond with or be comfortable being provided care by an AI. Regarding the influence of entertainment media on perceptions of AI, we found a significant relationship (p < 0.5) between people's beliefs about AI in entertainment media and their beliefs about AI in reality. Those who believe AI is realistically depicted in entertainment media were more likely to see AIs as potential emotional partners or apocalyptic robots than to imagine AIs taking over jobs or operating as surveillance tools.
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Affiliation(s)
- Karim Nader
- Department of Philosophy, The University of Texas at Austin, Austin, USA
| | - Paul Toprac
- Department of Computer Science, The University of Texas at Austin, Austin, USA
| | - Suzanne Scott
- Department of Radio-Television-Film, The University of Texas at Austin, Austin, USA
| | - Samuel Baker
- Department of English, The University of Texas at Austin, Austin, USA
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Shah P, Mishra D, Shanmugam M, Vighnesh MJ, Jayaraj H. Acceptability of artificial intelligence-based retina screening in general population. Indian J Ophthalmol 2022; 70:1140-1144. [PMID: 35326001 PMCID: PMC9240537 DOI: 10.4103/ijo.ijo_1840_21] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Purpose: A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening. Methods: This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO™, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants. Results: We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor’s visit can help in routine screening Conclusion: Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.
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Affiliation(s)
- Payal Shah
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - Divyansh Mishra
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - Mahesh Shanmugam
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - M J Vighnesh
- Sankara College of Optometry, Bengaluru, Karnataka, India
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Čartolovni A, Tomičić A, Lazić Mosler E. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int J Med Inform 2022; 161:104738. [PMID: 35299098 DOI: 10.1016/j.ijmedinf.2022.104738] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/11/2022] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Recent developments in the field of Artificial Intelligence (AI) applied to healthcare promise to solve many of the existing global issues in advancing human health and managing global health challenges. This comprehensive review aims not only to surface the underlying ethical and legal but also social implications (ELSI) that have been overlooked in recent reviews while deserving equal attention in the development stage, and certainly ahead of implementation in healthcare. It is intended to guide various stakeholders (eg. designers, engineers, clinicians) in addressing the ELSI of AI at the design stage using the Ethics by Design (EbD) approach. METHODS The authors followed a systematised scoping methodology and searched the following databases: Pubmed, Web of science, Ovid, Scopus, IEEE Xplore, EBSCO Search (Academic Search Premier, CINAHL, PSYCINFO, APA PsycArticles, ERIC) for the ELSI of AI in healthcare through January 2021. Data were charted and synthesised, and the authors conducted a descriptive and thematic analysis of the collected data. RESULTS After reviewing 1108 papers, 94 were included in the final analysis. Our results show a growing interest in the academic community for ELSI in the field of AI. The main issues of concern identified in our analysis fall into four main clusters of impact: AI algorithms, physicians, patients, and healthcare in general. The most prevalent issues are patient safety, algorithmic transparency, lack of proper regulation, liability & accountability, impact on patient-physician relationship and governance of AI empowered healthcare. CONCLUSIONS The results of our review confirm the potential of AI to significantly improve patient care, but the drawbacks to its implementation relate to complex ELSI that have yet to be addressed. Most ELSI refer to the impact on and extension of the reciprocal and fiduciary patient-physician relationship. With the integration of AIbased decision making tools, a bilateral patient-physician relationship may shift into a trilateral one.
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Affiliation(s)
- Anto Čartolovni
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia; School of Medicine, Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia.
| | - Ana Tomičić
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia.
| | - Elvira Lazić Mosler
- School of Medicine, Catholic University of Croatia, Ilica 242, 10 000 Zagreb, Croatia; General Hospital Dr. Ivo Pedišić, Sisak, Croatia.
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Isbanner S, O'Shaughnessy P, Steel D, Wilcock S, Carter S. The Australian Values and Attitudes on AI (AVA-AI) Study: Methodologically Innovative National Survey about Adopting Artificial Intelligence in Healthcare and Social Services (Preprint). J Med Internet Res 2022; 24:e37611. [PMID: 35994331 PMCID: PMC9446139 DOI: 10.2196/37611] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Sebastian Isbanner
- Social Marketing @ Griffith, Griffith Business School, Griffith University, Brisbane, Australia
| | - Pauline O'Shaughnessy
- School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia
| | - David Steel
- School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia
| | - Scarlet Wilcock
- Australian Research Council Centre of Excellence for Automated Decision-Making and Society, The University of Sydney Law School, The University of Sydney, Sydney, Australia
| | - Stacy Carter
- Australian Centre for Health Engagement Evidence and Values, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, Australia
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Jermutus E, Kneale D, Thomas J, Michie S. Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17550.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used. The realisation and success of AI depend heavily on people’s trust in its applications. Yet, influences on trust in healthcare AI (HAI) applications so far have been underexplored. The objective of this study was to identify aspects related to users, AI applications and the wider context influencing trust in HAI. Methods: We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched seven electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, ProQuest Dissertations & Thesis Global, PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. Results: Three studies, one mixed-method and two qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and context-related (AI company, media, users’ social network). The factors resulted in an updated logic model illustrating the relationship between these aspects. Conclusion: Trust in HAI depends on a variety of factors, both external and internal to AI applications. This study contributes to our understanding of what influences trust in HAI by highlighting key influences, as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.
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40
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Romero RA, Young SD. Public perceptions and implementation considerations on the use of artificial intelligence in health. J Eval Clin Pract 2022; 28:75-78. [PMID: 33977613 DOI: 10.1111/jep.13580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 04/23/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Romina A Romero
- Department of Emergency Medicine, University of California, Irvine, Irvine, CA, USA
| | - Sean D Young
- Department of Emergency Medicine, University of California, Irvine, Irvine, CA, USA.,University of California Institute for Prediction Technology, Department of Informatics, University of California, Irvine, Irvine, CA, USA
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41
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Chew HSJ, Achananuparp P. Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review. J Med Internet Res 2022; 24:e32939. [PMID: 35029538 PMCID: PMC8800095 DOI: 10.2196/32939] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/08/2021] [Accepted: 12/03/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. OBJECTIVE This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. METHODS A systematic scoping review was conducted according to the 5-stage framework by Arksey and O'Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. RESULTS Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. CONCLUSIONS The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Palakorn Achananuparp
- Living Analytics Research Centre, Singapore Management University, Singapore, Singapore
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Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health 2022; 8:20552076221116772. [PMID: 35983102 PMCID: PMC9380417 DOI: 10.1177/20552076221116772] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/13/2022] [Indexed: 12/23/2022] Open
Abstract
Objective The attitudes about the usage of artificial intelligence in healthcare are
controversial. Unlike the perception of healthcare professionals, the
attitudes of patients and their companions have been of less interest so
far. In this study, we aimed to investigate the perception of artificial
intelligence in healthcare among this highly relevant group along with the
influence of digital affinity and sociodemographic factors. Methods We conducted a cross-sectional study using a paper-based questionnaire with
patients and their companions at a German tertiary referral hospital from
December 2019 to February 2020. The questionnaire consisted of three
sections examining (a) the respondents’ technical affinity, (b) their
perception of different aspects of artificial intelligence in healthcare and
(c) sociodemographic characteristics. Results From a total of 452 participants, more than 90% already read or heard about
artificial intelligence, but only 24% reported good or expert knowledge.
Asked on their general perception, 53.18% of the respondents rated the use
of artificial intelligence in medicine as positive or very positive, but
only 4.77% negative or very negative. The respondents denied concerns about
artificial intelligence, but strongly agreed that artificial intelligence
must be controlled by a physician. Older patients, women, persons with lower
education and technical affinity were more cautious on the
healthcare-related artificial intelligence usage. Conclusions German patients and their companions are open towards the usage of artificial
intelligence in healthcare. Although showing only a mediocre knowledge about
artificial intelligence, a majority rated artificial intelligence in
healthcare as positive. Particularly, patients insist that a physician
supervises the artificial intelligence and keeps ultimate responsibility for
diagnosis and therapy.
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Affiliation(s)
- Sebastian J Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
| | - Andrea Blankenheim
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Alina Wahl
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Petra Hetfeld
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Julian Kunze
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Rolf Rossaint
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
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Cinelli M, Peruzzi A, Schmidt AL, Villa R, Costa E, Quattrociocchi W, Zollo F. Promoting engagement with quality communication in social media. PLoS One 2022; 17:e0275534. [PMID: 36227911 PMCID: PMC9560150 DOI: 10.1371/journal.pone.0275534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 09/16/2022] [Indexed: 11/14/2022] Open
Abstract
The COVID-19 pandemic made explicit the issues of communicating science in an information ecosystem dominated by social media platforms. One of the fundamental communication challenges of our time is to provide the public with reliable content and contrast misinformation. This paper investigates how social media can become an effective channel to promote engagement and (re)build trust. To measure the social response to quality communication, we conducted an experimental study to test a set of science communication recommendations on Facebook and Twitter. The experiment involved communication practitioners and social media managers from select countries in Europe, applying and testing such recommendations for five months. Here we analyse their feedback in terms of adoption and show that some differences emerge across platforms, topics, and recommendation categories. To evaluate these recommendations' effect on users, we measure their response to quality content, finding that the median engagement is generally higher, especially on Twitter. The results indicate that quality communication strategies may elicit positive feedback on social media. A data-driven and co-designed approach in developing counter-strategies is thus promising in tackling misinformation.
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Affiliation(s)
- Matteo Cinelli
- Ca’ Foscari University of Venice, Venice, Italy
- Sapienza University of Rome, Rome, Italy
| | | | | | | | | | | | - Fabiana Zollo
- Ca’ Foscari University of Venice, Venice, Italy
- * E-mail:
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Tran AQ, Nguyen LH, Nguyen HSA, Nguyen CT, Vu LG, Zhang M, Vu TMT, Nguyen SH, Tran BX, Latkin CA, Ho RCM, Ho CSH. Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians. Front Public Health 2021; 9:755644. [PMID: 34900904 PMCID: PMC8661093 DOI: 10.3389/fpubh.2021.755644] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/19/2021] [Indexed: 12/02/2022] Open
Abstract
Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs. Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention. Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.
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Affiliation(s)
- Anh Quynh Tran
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Long Hoang Nguyen
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | | | - Cuong Tat Nguyen
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam.,Faculty of Medicine, Duy Tan University, Da Nang, Vietnam
| | - Linh Gia Vu
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam.,Faculty of Medicine, Duy Tan University, Da Nang, Vietnam
| | - Melvyn Zhang
- National Addictions Management Service (NAMS), Institute of Mental Health, Singapore, Singapore
| | | | - Son Hoang Nguyen
- Center of Excellence in Evidence-Based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Bach Xuan Tran
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.,Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Carl A Latkin
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Roger C M Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Singapore
| | - Cyrus S H Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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45
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Ploug T, Sundby A, Moeslund TB, Holm S. Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey. J Med Internet Res 2021; 23:e26611. [PMID: 34898454 PMCID: PMC8713089 DOI: 10.2196/26611] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/31/2021] [Accepted: 11/11/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public's interests in such features of AI. OBJECTIVE This study elicited the public's preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. METHODS We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents' views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. RESULTS Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents' trust in health and technology, and respondents' fears and hopes regarding AI, do not play a significant role in the majority of cases. CONCLUSIONS The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.
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Affiliation(s)
- Thomas Ploug
- Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark
| | - Anna Sundby
- Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark
| | - Thomas B Moeslund
- Visual Analysis and Perception Lab, Aalborg University, Aalborg, Denmark
| | - Søren Holm
- Centre for Social Ethics and Policy, University of Manchester, Manchester, United Kingdom
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Cushnan D, Berka R, Bertolli O, Williams P, Schofield D, Joshi I, Favaro A, Halling-Brown M, Imreh G, Jefferson E, Sebire NJ, Reilly G, Rodrigues JCL, Robinson G, Copley S, Malik R, Bloomfield C, Gleeson F, Crotty M, Denton E, Dickson J, Leeming G, Hardwick HE, Baillie K, Openshaw PJ, Semple MG, Rubin C, Howlett A, Rockall AG, Bhayat A, Fascia D, Sudlow C, Jacob J. Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic. Digit Health 2021; 7:20552076211048654. [PMID: 34868617 PMCID: PMC8637703 DOI: 10.1177/20552076211048654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/07/2021] [Indexed: 12/27/2022] Open
Abstract
The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the
unprecedented collection of health data to support research. Historically,
coordinating the collation of such datasets on a national scale has been
challenging to execute for several reasons, including issues with data privacy,
the lack of data reporting standards, interoperable technologies, and
distribution methods. The coronavirus SARS-CoV-2 disease pandemic has
highlighted the importance of collaboration between government bodies,
healthcare institutions, academic researchers and commercial companies in
overcoming these issues during times of urgency. The National COVID-19 Chest
Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey
NHS Foundation Trust and Faculty, is an example of such a national initiative.
Here, we summarise the experiences and challenges of setting up the National
COVID-19 Chest Imaging Database, and the implications for future ambitions of
national data curation in medical imaging to advance the safe adoption of
artificial intelligence in healthcare.
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Affiliation(s)
| | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust, UK.,CVSSP, University of Surrey, UK
| | | | - Emily Jefferson
- Health Data Research UK, UK.,Health Informatics Centre (HIC), School of Medicine, University of Dundee, UK
| | | | | | | | - Graham Robinson
- Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, UK
| | - Susan Copley
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK
| | - Rizwan Malik
- Department of Radiology, Bolton NHS Foundation Trust, UK
| | - Claire Bloomfield
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | - Fergus Gleeson
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | | | - Erika Denton
- Norfolk and Norwich University Hospital Foundation Trust, UK
| | | | - Gary Leeming
- Institute of Population Health, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Hayley E Hardwick
- National Institute of Health Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections, UK
| | | | | | - Malcolm G Semple
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Caroline Rubin
- Department of Radiology, University Hospital Southampton NHS Foundation Trust, UK
| | | | - Andrea G Rockall
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK.,Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK
| | - Ayub Bhayat
- NHS Arden & Greater East Midlands Commissioning Support Unit, UK
| | | | - Cathie Sudlow
- British Heart Foundation Data Science Centre Led by Health Data Research UK, UK
| | | | - Joseph Jacob
- Department of Respiratory Medicine, University College London, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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47
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Nadarzynski T, Puentes V, Pawlak I, Mendes T, Montgomery I, Bayley J, Ridge D. Barriers and facilitators to engagement with artificial intelligence (AI)-based chatbots for sexual and reproductive health advice: a qualitative analysis. Sex Health 2021; 18:385-393. [PMID: 34782055 DOI: 10.1071/sh21123] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/19/2021] [Indexed: 01/13/2023]
Abstract
Background The emergence of artificial intelligence (AI) provides opportunities for demand management of sexual and reproductive health services. Conversational agents/chatbots are increasingly common, although little is known about how this technology could aid services. This study aimed to identify barriers and facilitators for engagement with sexual health chatbots to advise service developers and related health professionals. Methods In January-June 2020, we conducted face-to-face, semi-structured and online interviews to explore views on sexual health chatbots. Participants were asked to interact with a chatbot, offering advice on sexually transmitted infections (STIs) and relevant services. Participants were UK-based and recruited via social media. Data were recorded, transcribed verbatim and analysed thematically. Results Forty participants (aged 18-50 years; 64% women, 77% heterosexual, 58% white) took part. Many thought chatbots could aid sex education, providing useful information about STIs and sign-posting to sexual health services in a convenient, anonymous and non-judgemental way. Some compared chatbots to health professionals or Internet search engines and perceived this technology as inferior, offering constrained content and interactivity, limiting disclosure of personal information, trust and perceived accuracy of chatbot responses. Conclusions Despite mixed attitudes towards chatbots, this technology was seen as useful for anonymous sex education but less suitable for matters requiring empathy. Chatbots may increase access to clinical services but their effectiveness and safety need to be established. Future research should identify which chatbots designs and functions lead to optimal engagement with this innovation.
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Affiliation(s)
- Tom Nadarzynski
- School of Social Sciences, University of Westminster, London, UK
| | - Vannesa Puentes
- Science, Engineering and Computing Faculty, Kingston University, London, UK
| | - Izabela Pawlak
- School of Social Sciences, University of Westminster, London, UK
| | - Tania Mendes
- School of Social Sciences, University of Westminster, London, UK
| | | | | | - Damien Ridge
- School of Social Sciences, University of Westminster, London, UK
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Martinho A, Kroesen M, Chorus C. A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence. Artif Intell Med 2021; 121:102190. [PMID: 34763805 DOI: 10.1016/j.artmed.2021.102190] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 09/22/2021] [Accepted: 09/29/2021] [Indexed: 12/23/2022]
Abstract
Artificial Intelligence (AI) is moving towards the health space. It is generally acknowledged that, while there is great promise in the implementation of AI technologies in healthcare, it also raises important ethical issues. In this study we surveyed medical doctors based in The Netherlands, Portugal, and the U.S. from a diverse mix of medical specializations about the ethics surrounding Health AI. Four main perspectives have emerged from the data representing different views about this matter. The first perspective (AI is a helpful tool: Let physicians do what they were trained for) highlights the efficiency associated with automation, which will allow doctors to have the time to focus on expanding their medical knowledge and skills. The second perspective (Rules & Regulations are crucial: Private companies only think about money) shows strong distrust in private tech companies and emphasizes the need for regulatory oversight. The third perspective (Ethics is enough: Private companies can be trusted) puts more trust in private tech companies and maintains that ethics is sufficient to ground these corporations. And finally the fourth perspective (Explainable AI tools: Learning is necessary and inevitable) emphasizes the importance of explainability of AI tools in order to ensure that doctors are engaged in the technological progress. Each perspective provides valuable and often contrasting insights about ethical issues that should be operationalized and accounted for in the design and development of AI Health.
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Affiliation(s)
| | | | - Caspar Chorus
- Delft University of Technology, Delft, the Netherlands
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49
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Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
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What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials. PLoS One 2021; 16:e0257732. [PMID: 34669734 PMCID: PMC8528275 DOI: 10.1371/journal.pone.0257732] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 09/07/2021] [Indexed: 12/15/2022] Open
Abstract
Rapid advances in machine learning and related techniques have increased optimism about self-driving cars, autonomous surgery, and other uses of artificial intelligence (AI). But adoption of these technologies is not simply a matter of breakthroughs in the design and training of algorithms. Regulators around the world will have to make a litany of choices about law and policy surrounding AI. To advance knowledge of how they will make these choices, we draw on a unique survey pool—690 local officials in the United States—a representative sample of U.S. local officials. These officials will make many of the decisions about AI adoption, from government use to regulation, given the decentralized structure of the United States. The results show larger levels of support for autonomous vehicles than autonomous surgery. Moreover, those that used ridesharing apps prior to the COVID-19 pandemic are significantly more supportive of autonomous vehicles. We also find that self-reported familiarity with AI is correlated with increased approval of AI uses in a variety of areas, including facial recognition, natural disaster impact planning, and even military surveillance. Related, those who expressed greater opposition to AI adoption also appear more concerned about trade-offs between privacy and information and bias in algorithms. Finally, the explanatory logic used by respondents varies based on gender and prior experience with AI, which we demonstrate with quantitative text analysis.
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