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Liu J, Du H, Huang L, Xie W, Liu K, Zhang X, Chen S, Zhang Y, Li D, Pan H. AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:38832-38851. [PMID: 39016521 DOI: 10.1021/acsami.4c07665] [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: 07/18/2024]
Abstract
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
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Affiliation(s)
- Junchi Liu
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Hanze Du
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Lei Huang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Wangni Xie
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Kexuan Liu
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Xue Zhang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Yuan Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Daowei Li
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Ghadiri P, Yaffe MJ, Adams AM, Abbasgholizadeh-Rahimi S. Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health. BMC PRIMARY CARE 2024; 25:215. [PMID: 38872128 PMCID: PMC11170885 DOI: 10.1186/s12875-024-02417-1] [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: 03/15/2023] [Accepted: 05/06/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks. METHODS We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis. RESULTS We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm. CONCLUSION This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.
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Affiliation(s)
- Pooria Ghadiri
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
- Mila-Quebec AI Institute, Montréal, QC, Canada
| | - Mark J Yaffe
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
- St. Mary's Hospital Center of the Integrated University Centre for Health and Social Services of West Island of Montreal, Montréal, QC, Canada
| | - Alayne Mary Adams
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada.
- Mila-Quebec AI Institute, Montréal, QC, Canada.
- Lady Davis Institute for Medical Research (LDI), Jewish General Hospital, Montréal, QC, Canada.
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Kalanjiyam GP, Chandramohan T, Raman M, Kalyanasundaram H. Artificial intelligence: a new cutting-edge tool in spine surgery. Asian Spine J 2024; 18:458-471. [PMID: 38917854 PMCID: PMC11222879 DOI: 10.31616/asj.2023.0382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/07/2024] [Accepted: 01/11/2024] [Indexed: 06/27/2024] Open
Abstract
The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.
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Affiliation(s)
- Guna Pratheep Kalanjiyam
- Spine Surgery Unit, Department of Orthopaedics, Meenakshi Mission Hospital and Research Centre, Madurai,
India
| | - Thiyagarajan Chandramohan
- Department of Orthopaedics, Government Stanley Medical College, Chennai,
India
- Department of Emergency Medicine, Government Stanley Medical College, Chennai,
India
| | - Muthu Raman
- Department of Orthopaedics, Tenkasi Government Hospital, Tenkasi,
India
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Taciuc IA, Dumitru M, Vrinceanu D, Gherghe M, Manole F, Marinescu A, Serboiu C, Neagos A, Costache A. Applications and challenges of neural networks in otolaryngology (Review). Biomed Rep 2024; 20:92. [PMID: 38765859 PMCID: PMC11099604 DOI: 10.3892/br.2024.1781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 05/22/2024] Open
Abstract
Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.
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Affiliation(s)
- Iulian-Alexandru Taciuc
- Department of Pathology, ‘Carol Davila’ University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Mihai Dumitru
- Department of ENT, ‘Carol Davila’ University of Medicine and Pharmacy, 050751 Bucharest, Romania
| | - Daniela Vrinceanu
- Department of ENT, ‘Carol Davila’ University of Medicine and Pharmacy, 050751 Bucharest, Romania
| | - Mirela Gherghe
- Department of Nuclear Medicine, ‘Carol Davila’ University of Medicine and Pharmacy, 022328 Bucharest, Romania
| | - Felicia Manole
- Department of ENT, Faculty of Medicine University of Oradea, 410073 Oradea, Romania
| | - Andreea Marinescu
- Department of Radiology and Medical Imaging ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania
| | - Crenguta Serboiu
- Department of Cell Biology, Molecular and Histology, ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania
| | - Adriana Neagos
- Department of ENT, ‘George Emil Palade’ University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Mures, Romania
| | - Adrian Costache
- Department of Pathology, ‘Carol Davila’ University of Medicine and Pharmacy, 020021 Bucharest, Romania
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Denecke K, May R, Rivera Romero O. Potential of Large Language Models in Health Care: Delphi Study. J Med Internet Res 2024; 26:e52399. [PMID: 38739445 PMCID: PMC11130776 DOI: 10.2196/52399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/10/2023] [Accepted: 04/19/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. OBJECTIVE The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. METHODS We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. RESULTS The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. CONCLUSIONS Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.
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Affiliation(s)
| | - Richard May
- Harz University of Applied Sciences, Wernigerode, Germany
| | - Octavio Rivera Romero
- Instituto de Ingeniería Informática (I3US), Universidad de Sevilla, Sevilla, Spain
- Department of Electronic Technology, Universidad de Sevilla, Sevilla, Spain
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Waheed MA, Liu L. Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization. JMIR AI 2024; 3:e40781. [PMID: 38875531 PMCID: PMC11063883 DOI: 10.2196/40781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 05/25/2023] [Accepted: 03/07/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles. OBJECTIVE This study aims to determine the impact of AI on the management and practices of Qatar's Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians' job roles, including associated risks and ethical ramifications from their perspective. METHODS We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses. RESULTS Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians. CONCLUSIONS This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.
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Affiliation(s)
| | - Lu Liu
- Bath Business School, Bath Spa University, Bath, United Kingdom
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Öberg Westin M, Wikberg C, Björkelund C, Ariai N, Lindahl AC, Hange D. Laying the Groundwork: A Pilot Study Exploring Improved Endometriosis Management Strategies in Primary Healthcare. Int J Gen Med 2024; 17:1325-1333. [PMID: 38596645 PMCID: PMC11003436 DOI: 10.2147/ijgm.s375279] [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: 10/03/2023] [Accepted: 02/14/2024] [Indexed: 04/11/2024] Open
Abstract
Objective To evaluate if a brief educational intervention for primary health care staff regarding endometriosis gives sustainable knowledge of endometriosis symptoms and if a brief questionnaire for women seeking care for common symptoms is feasible in the Swedish primary care context. Design Pilot study. Setting 3 primary care centres (PCCs) in Sweden. Exploring knowledge among staff about endometriosis at baseline and 3 months after an information workshop. Evaluation of feasibility of a brief questionnaire for women seeking care for common symptoms For PCC personnel: knowledge about endometriosis at baseline and after 3 months. For patient questionnaire: whether adequate, understandable, acceptable, and feasible. Participants Females in primary care centre waiting room, and staff members at participating PCCs. Results The knowledge level of endometriosis was improved or sustained after 3 months compared to baseline among staff at PCCs. Over 90% of the patients stated that the questionnaire was adequate, understandable, acceptable, and feasible. Conclusion We found that an educational programme improved the staff's knowledge about endometriosis. The programme together with the patient questionnaire could be a way to enhance knowledge about endometriosis among PCCs. This combined effort might facilitate earlier detection and treatment of women with endometriosis.
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Affiliation(s)
- Maria Öberg Westin
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Carl Wikberg
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Research, Education, Development & Innovation, Primary Health Care, Region Västra Götaland, Gothenburg, Sweden
| | - Cecilia Björkelund
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nashmil Ariai
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ann-Christine Lindahl
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dominique Hange
- Primary Health Care/School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Research, Education, Development & Innovation, Primary Health Care, Region Västra Götaland, Gothenburg, Sweden
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Pinto-de-Sá R, Sousa-Pinto B, Costa-de-Oliveira S. Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review. Antibiotics (Basel) 2024; 13:307. [PMID: 38666983 PMCID: PMC11047419 DOI: 10.3390/antibiotics13040307] [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: 02/20/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024] Open
Abstract
Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP.
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Affiliation(s)
- Rafaela Pinto-de-Sá
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
| | - Bernardo Sousa-Pinto
- Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Sofia Costa-de-Oliveira
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Lin GSS, Tan WW, Hashim H. Students' perceptions towards the ethical considerations of using artificial intelligence algorithms in clinical decision-making. Br Dent J 2024:10.1038/s41415-024-7184-3. [PMID: 38491204 DOI: 10.1038/s41415-024-7184-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 03/18/2024]
Abstract
Aim The present study aimed to explore the perceptions of dental students regarding the ethical considerations associated with the use of artificial intelligence (AI) algorithms in clinical decision-making.Methods All the undergraduate clinical-year dental students were invited to take part in the study. A validated online questionnaire which consisted of 21 closed-ended questions (five-point Likert scales) was distributed to the students to evaluate their perceptions on the topic. Mean perception scores of the students from different years were analysed using a one-way ANOVA test, while independent t-tests were used to compare the scores between sexes.Results In total, 165 students participated in the present study. The mean age of the respondents was 23.3 (± 1.38) years and the majority were female, Chinese students. Respondents showed positive perceptions throughout all three domains. Uniform and comparable perceptions were seen across various academic years and sexes, with female respondents expressing stronger agreement regarding patient consent and privacy prioritisation.Conclusion Undergraduate clinical dental students generally showed positive perceptions regarding the ethical considerations associated with the integration of AI algorithms in clinical decision-making. It is essential to address these ethical considerations to ensure that AI benefits patient outcomes while upholding fundamental ethical principles and patient-centred care.
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Affiliation(s)
- Galvin Sim Siang Lin
- Department of Restorative Dentistry, Kulliyyah of Dentistry, International Islamic University Malaysia, 25200, Pahang, Malaysia.
| | - Wen Wu Tan
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
| | - Hasnah Hashim
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
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Madi M, Nielsen S, Schweitzer M, Siebert M, Körner D, Langensiepen S, Stephan A, Meyer G. Acceptance of a robotic system for nursing care: a cross-sectional survey with professional nurses, care recipients and relatives. BMC Nurs 2024; 23:179. [PMID: 38486244 PMCID: PMC10938668 DOI: 10.1186/s12912-024-01849-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND The end-users' acceptance is a core concept in the development, implementation and evaluation of new systems like robotic systems in daily nursing practice. So far, studies have shown various findings concerning the acceptance of systems that are intended to assist people with support or care needs. Not much has been reported on the acceptance of robots that provide direct physical assistance to nurses in bedside care. Therefore, this study aimed to investigate the acceptance along with ethical implications of the prototype of an assistive robotic arm aiming to support nurses in bedside care, from the perspective of nurses, care recipients and their relatives. METHODS A cross-sectional survey design was applied at an early stage in the technological development of the system. Professional nurses, care recipients and relatives were recruited from a university hospital and a nursing home in Germany. The questionnaire was handed out following either a video or a live demonstration of the lab prototype and a subsequent one-to-one follow-up discussion. Data analysis was performed descriptively. RESULTS A total of 67 participants took part in the study. The rejection of specified ethical concerns across all the respondents was 77%. For items related to both perceived usefulness and intention to use, 75% of ratings across all the respondents were positive. In the follow-up discussions, the participants showed interest and openness toward the prototype, although there were varying opinions on aspects such as size, appearance, velocity, and potential impact on workload. CONCLUSIONS Regarding the current state of development, the acceptance among the participants was high, and ethical concerns were relatively minor. Moving forward, it would be beneficial to explore the acceptance in further developmental stages of the system, particularly when the usability is tested.
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Affiliation(s)
- Murielle Madi
- Institute of Health and Nursing Science, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Straße 8, 06112, Halle (Saale), Germany.
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Svenja Nielsen
- Institute of Health and Nursing Science, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Straße 8, 06112, Halle (Saale), Germany.
| | - Mona Schweitzer
- Institute of Health and Nursing Science, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Straße 8, 06112, Halle (Saale), Germany
| | - Maximilian Siebert
- Institute of Applied Medical Engineering, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Daniel Körner
- Institute of Applied Medical Engineering, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Sina Langensiepen
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Astrid Stephan
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Gabriele Meyer
- Institute of Health and Nursing Science, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Straße 8, 06112, Halle (Saale), Germany
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Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urol Oncol 2024; 42:48-56. [PMID: 38101991 DOI: 10.1016/j.urolonc.2023.11.019] [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: 11/11/2023] [Accepted: 11/25/2023] [Indexed: 12/17/2023]
Abstract
Recent progress in the realm of artificial intelligence has shown effectiveness in various industries, particularly within the healthcare sector. However, there are limited insights on existing studies regarding ethical, social, privacy, and technological aspects of AI in the health sector, which is the gap our study aims to fill. This study aimed to synthesize empirical studies on the challenges and opportunities of using AI by conducting a systematic review. We reviewed 33 articles published between 2015 and 2022 in the PubMed, IEEE Xplore, and Science Direct databases. The results show that artificial intelligence has the promise of improving health care and faces obstacles when implemented. Most of the reviewed studies indicated that the use of AI provides several opportunities, including teamwork and decision-making, technological advancement, diagnosis and patient monitoring, drug development, and virtual health assistance. However, the findings show that the use of AI in the health sector hinders multifaceted challenges, including ethical and privacy-related issues, lack of awareness, unreliability of technology, and professional liability. The findings highlight that artificial intelligence has the potential to transform healthcare and that addressing these challenges is crucial to fully utilize its potential.
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Affiliation(s)
- Betelhem Zewdu Wubineh
- Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland; School of Computing and Informatics, Wachemo University, Hosaena, Ethiopia.
| | - Fitsum Gizachew Deriba
- School of Computing, University of Eastern Finland, Joensuu, Finland; School of Computing and Informatics, Wachemo University, Hosaena, Ethiopia
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12
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Kueper JK, Emu M, Banbury M, Bjerre LM, Choudhury S, Green M, Pimlott N, Slade S, Tsuei SH, Sisler J. Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group. CANADIAN FAMILY PHYSICIAN MEDECIN DE FAMILLE CANADIEN 2024; 70:161-168. [PMID: 38499374 PMCID: PMC11280631 DOI: 10.46747/cfp.7003161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.
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Affiliation(s)
- Jacqueline K Kueper
- Adjunct Research Professor in the Department of Epidemiology and Biostatistics in the Schulich School of Medicine and Dentistry at Western University in London, Ont
| | - Mahzabeen Emu
- Doctoral candidate in the School of Computing at Queen's University in Kingston, Ont
| | - Mark Banbury
- Executive Director, Information and Technology Services, at the College of Family Physicians of Canada
| | - Lise M Bjerre
- Associate Professor at the University of Ottawa in Ontario and Chair in Family Medicine at the Institut du Savoir Montfort in Ottawa
| | - Salimur Choudhury
- Associate Professor in the School of Computing at Queen's University
| | - Michael Green
- Professor in the Department of Family Medicine at Queen's University and President of the College of Family Physicians of Canada
| | - Nicholas Pimlott
- Professor in the Department of Family and Community Medicine in the Temerty Faculty of Medicine at the University of Toronto in Ontario and Editor of Canadian Family Physician
| | - Steve Slade
- Research Director at the College of Family Physicians of Canada
| | - Sian H Tsuei
- Clinical Assistant Professor in the Department of Family Practice at the University of British Columbia in Vancouver and Adjunct Professor in the Faculty of Health Sciences at Simon Fraser University in Victoria, BC
| | - Jeff Sisler
- Former Executive Director of Professional Development and Practice Support at the College of Family Physicians of Canada
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13
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Pupic N, Ghaffari-Zadeh A, Hu R, Singla R, Darras K, Karwowska A, Forster BB. An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS DIGITAL HEALTH 2023; 2:e0000255. [PMID: 38011214 PMCID: PMC10681314 DOI: 10.1371/journal.pdig.0000255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/14/2023] [Indexed: 11/29/2023]
Abstract
The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.
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Affiliation(s)
- Nikola Pupic
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Aryan Ghaffari-Zadeh
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Ricky Hu
- Faculty of Medicine, Queen's University, Ontario, Kingston, Canada
| | - Rohit Singla
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Kathryn Darras
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
| | - Anna Karwowska
- Association of Faculties of Medicine of Canada, Ontario, Ottawa, Canada
- Faculty of Medicine, Department of Pediatrics, University of Ottawa, Ontario, Ottawa, Canada
| | - Bruce B Forster
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
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14
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Erku D, Khatri R, Endalamaw A, Wolka E, Nigatu F, Zewdie A, Assefa Y. Digital Health Interventions to Improve Access to and Quality of Primary Health Care Services: A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6854. [PMID: 37835125 PMCID: PMC10572344 DOI: 10.3390/ijerph20196854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/15/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
Abstract
Global digital technology advances offer the potential to enhance primary health care (PHC) quality, reach, and efficiency, driving toward universal health coverage (UHC). This scoping review explored how digital health solutions aid PHC delivery and UHC realization by examining the context, mechanisms, and outcomes of eHealth interventions. A comprehensive literature search was conducted, capturing qualitative and quantitative studies, process evaluations, and systematic or scoping reviews. Our analysis of 65 articles revealed that a well-functioning digital ecosystem-featuring adaptable, interoperable digital tools, robust Information and Communications Technology foundations, and enabling environments-is pivotal for eHealth interventions' success. Facilities with better digital literacy, motivated staff, and adequate funding demonstrated a higher adoption of eHealth technologies, leading to improved, coordinated service delivery and higher patient satisfaction. However, eHealth's potential is often restricted by existing socio-cultural norms, geographical inequities in technology access, and digital literacy disparities. Our review underscores the importance of considering the digital ecosystem's readiness, user behavior, broader health system requirements, and PHC capacity for adopting digital solutions while assessing digital health interventions' impact.
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Affiliation(s)
- Daniel Erku
- Centre for Applied Health Economics, School of Medicine, Griffith University, Nathan, QLD 4111, Australia
- Menzies Health Institute Queensland, Griffith University, Nathan, QLD 4111, Australia
- School of Public Health, The University of Queensland, Brisbane, QLD 4072, Australia; (R.K.); (A.E.); (Y.A.)
| | - Resham Khatri
- School of Public Health, The University of Queensland, Brisbane, QLD 4072, Australia; (R.K.); (A.E.); (Y.A.)
| | - Aklilu Endalamaw
- School of Public Health, The University of Queensland, Brisbane, QLD 4072, Australia; (R.K.); (A.E.); (Y.A.)
- College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Eskinder Wolka
- International Institute for Primary Health Care in Ethiopia, Addis Ababa, Ethiopia; (E.W.); (F.N.); (A.Z.)
| | - Frehiwot Nigatu
- International Institute for Primary Health Care in Ethiopia, Addis Ababa, Ethiopia; (E.W.); (F.N.); (A.Z.)
| | - Anteneh Zewdie
- International Institute for Primary Health Care in Ethiopia, Addis Ababa, Ethiopia; (E.W.); (F.N.); (A.Z.)
| | - Yibeltal Assefa
- School of Public Health, The University of Queensland, Brisbane, QLD 4072, Australia; (R.K.); (A.E.); (Y.A.)
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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Gebrye T, Fatoye F, Mbada C, Hakimi Z. A scoping review on quality assessment tools used in systematic reviews and meta-analysis of real-world studies. Rheumatol Int 2023; 43:1573-1581. [PMID: 37326665 PMCID: PMC10348931 DOI: 10.1007/s00296-023-05354-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/26/2023] [Indexed: 06/17/2023]
Abstract
Risk of bias tools is important in identifying inherent methodical flaws and for generating evidence in studies involving systematic reviews (SRs) and meta-analyses (MAs), hence the need for sensitive and study-specific tools. This study aimed to review quality assessment (QA) tools used in SRs and MAs involving real-world data. Electronic databases involving PubMed, Allied and Complementary Medicine Database, Cumulated Index to Nursing and Allied Health Literature, and MEDLINE were searched for SRs and MAs involving real-world data. Search was delimited to articles published in English, and between inception to 20 of November 2022 following the SRs and MAs extension for scoping checklist. Sixteen articles on real-world data published between 2016 and 2021 that reported their methodological quality met the inclusion criteria. Seven of these articles were observational studies, while the others were of interventional type. Overall, 16 QA tools were identified. Except one, all the QA tools employed in SRs and MAs involving real-world data are generic, and only three of these were validated. Generic QA tools are mostly used for real-world data SRs and MAs, while no validated and reliable specific tool currently exist. Thus, there is need for a standardized and specific QA tool of SRs and MAs for real-world data.
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Affiliation(s)
- Tadesse Gebrye
- Department of Health Professions, Faculty of Health, Psychology, and Social Care, Manchester Metropolitan University, Brooks Building, Birley Fields Campus, Bonsall Street, 53 Bonsall Street, Manchester, M15 6GX UK
| | - Francis Fatoye
- Department of Health Professions, Faculty of Health, Psychology, and Social Care, Manchester Metropolitan University, Brooks Building, Birley Fields Campus, Bonsall Street, 53 Bonsall Street, Manchester, M15 6GX UK
- Lifestyle Diseases, Faculty of Health Sciences, North-West University, Mahikeng, South Africa
| | - Chidozie Mbada
- Department of Health Professions, Faculty of Health, Psychology, and Social Care, Manchester Metropolitan University, Brooks Building, Birley Fields Campus, Bonsall Street, 53 Bonsall Street, Manchester, M15 6GX UK
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17
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Rokhshad R, Ducret M, Chaurasia A, Karteva T, Radenkovic M, Roganovic J, Hamdan M, Mohammad-Rahimi H, Krois J, Lahoud P, Schwendicke F. Ethical considerations on artificial intelligence in dentistry: A framework and checklist. J Dent 2023; 135:104593. [PMID: 37355089 DOI: 10.1016/j.jdent.2023.104593] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
| | - Maxime Ducret
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Faculty of Odontology, University Claude Bernard Lyon Il, University of Lyon, Lyon, France
| | - Akhilanand Chaurasia
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India; Faculty of Dentistry, University of Puthisashtra, Combodia
| | - Teodora Karteva
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University Plovdiv, Bulgaria
| | - Miroslav Radenkovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Serbia
| | - Jelena Roganovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology in Dentistry, School of Dental medicine, University of Belgrade, Serbia
| | - Manal Hamdan
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; General Dental Sciences Department, Marquette University School of Dentistry, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pierre Lahoud
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Oral and MaxilloFacial Surgery & Imaging and Pathology- OMFS-IMPATH Research Group, KU Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Belgium
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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18
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Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. Am J Transl Res 2023; 15:4820-4828. [PMID: 37560249 PMCID: PMC10408516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Human society has entered the age of artificial intelligence, medical practice and medical education are undergoing profound changes. Artificial intelligence (AI) is now applied in many industries, particularly in healthcare and medical education, where it deeply intersects. The purpose of this paper is to overview the current situation and problems of "AI+medicine/medical" education and to provide our own perspective on the current predicament. METHODS We searched PubMed, Embase, Cochrane and CNKI databases to assess the literature on AI+medical/medical education from 2017 to July 2022. The main inclusion criteria include literature describing the current situation or predicament of "AI+medical/medical education". RESULTS Studies have shown that the current application of AI in medical education is focused on clinical specialty training and continuing education, with the main application areas being radiology, diagnostics, surgery, cardiology, and dentistry. The main role is to assist physicians to improve their efficiency and accuracy. In addition, the field of combining AI with medicine/medical education is steadily expanding, and the most urgent need is for policy makers, experts in the medical field, AI and education, and experts in other fields to come together to reach consensus on ethical issues and develop regulatory standards. Our study also found that most medical students are positive about adding AI-related courses to the existing medical curriculum. Finally, the quality of research on "AI+medical/medical education" is poor. CONCLUSION In the context of the COVID-19 pandemic, our study provides an innovative systematic review of the latest "AI+medicine/medical curriculum". Since the AI+medicine curriculum is not yet regulated, we have made some suggestions.
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Affiliation(s)
- Li Sun
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiang 157011, Heilongjiang, China
- Heilongjiang Key Laboratory of Ischemic Stroke Prevention and TreatmentMudanjiang 157011, Heilongjiang, China
| | - Changhao Yin
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiang 157011, Heilongjiang, China
- Heilongjiang Key Laboratory of Ischemic Stroke Prevention and TreatmentMudanjiang 157011, Heilongjiang, China
| | - Qiuling Xu
- Department of Physiology, Mudanjiang Medical UniversityMudanjiang 157011, Heilongjiang, China
| | - Weina Zhao
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiang 157011, Heilongjiang, China
- Heilongjiang Key Laboratory of Ischemic Stroke Prevention and TreatmentMudanjiang 157011, Heilongjiang, China
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Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med 2023; 6:111. [PMID: 37301946 DOI: 10.1038/s41746-023-00852-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.
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Affiliation(s)
- Sophie Isabelle Lambert
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Murielle Madi
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Saša Sopka
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andrea Lenes
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Hendrik Stange
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Claus-Peter Buszello
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Astrid Stephan
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Fliedner University of Applied Sciences, Geschwister-Aufricht-Straße, 940489, Düsseldorf, Germany
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Jeong K, Mallard AR, Coombe L, Ward J. Artificial intelligence and prediction of cardiometabolic disease: Systematic review of model performance and potential benefits in indigenous populations. Artif Intell Med 2023; 139:102534. [PMID: 37100506 DOI: 10.1016/j.artmed.2023.102534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 11/20/2022] [Accepted: 03/21/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples. METHODS We searched peer-reviewed literature using terms associated with AI machine learning, PHC, CMD, and Indigenous peoples. RESULTS We identified 13 suitable studies for inclusion in this review. Median total number of participants was 19,270 (range 911-2,994,837). The most common algorithms used in machine learning in this setting were support vector machine, random forest, and decision tree learning. Twelve studies used the area under the receiver operating characteristic curve (AUC) to measure performance. Two studies reported an AUC of >0.9. Six studies had an AUC score between 0.9 and 0.8, 4 studies had an AUC score between 0.8 and 0.7. 1 study reported an AUC score between 0.7 and 0.6. Risk of bias was observed in 10 (77 %) studies. CONCLUSION AI machine learning and risk prediction models show moderate to excellent discriminatory ability over traditional statistical models in predicting CMD. This technology could help address the needs of urban Indigenous peoples by predicting CMD early and more rapidly than conventional methods.
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Henckert D, Malorgio A, Schweiger G, Raimann FJ, Piekarski F, Zacharowski K, Hottenrott S, Meybohm P, Tscholl DW, Spahn DR, Roche TR. Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative-Quantitative Study. J Clin Med 2023; 12:jcm12062096. [PMID: 36983099 PMCID: PMC10054443 DOI: 10.3390/jcm12062096] [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: 02/01/2023] [Revised: 02/16/2023] [Accepted: 03/01/2023] [Indexed: 03/30/2023] Open
Abstract
Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44%) and cons (59 of 105, 56%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.
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Affiliation(s)
- David Henckert
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Amos Malorgio
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Giovanna Schweiger
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Florian J Raimann
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Florian Piekarski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Kai Zacharowski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Sebastian Hottenrott
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - Patrick Meybohm
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - David W Tscholl
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Donat R Spahn
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Tadzio R Roche
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
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Tanaka M, Matsumura S, Bito S. “What are the roles and competencies of doctors in the artificial intelligence implementation society?: A qualitative analysis through physician interview” (Preprint). JMIR Form Res 2023; 7:e46020. [PMID: 37200074 DOI: 10.2196/46020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/31/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is a term used to describe the use of computers and technology to emulate human intelligence mechanisms. Although AI is known to affect health services, the impact of information provided by AI on the patient-physician relationship in actual practice is unclear. OBJECTIVE The purpose of this study is to investigate the effect of introducing AI functions into the medical field on the role of the physician or physician-patient relationship, as well as potential concerns in the AI era. METHODS We conducted focus group interviews in Tokyo's suburbs with physicians recruited through snowball sampling. The interviews were conducted in accordance with the questions listed in the interview guide. A verbatim transcript recording of all interviews was qualitatively analyzed using content analysis by all authors. Similarly, extracted code was grouped into subcategories, categories, and then core categories. We continued interviewing, analyzing, and discussing until we reached data saturation. In addition, we shared the results with all interviewees and confirmed the content to ensure the credibility of the analysis results. RESULTS A total of 9 participants who belonged to various clinical departments in the 3 groups were interviewed. The same interviewers conducted the interview as the moderator each time. The average group interview time for the 3 groups was 102 minutes. Content saturation and theme development were achieved with the 3 groups. We identified three core categories: (1) functions expected to be replaced by AI, (2) functions still expected of human physicians, and (3) concerns about the medical field in the AI era. We also summarized the roles of physicians and patients, as well as the changes in the clinical environment in the age of AI. Some of the current functions of the physician were primarily replaced by AI functions, while others were inherited as the functions of the physician. In addition, "functions extended by AI" obtained by processing massive amounts of data will emerge, and a new role for physicians will be created to deal with them. Accordingly, the importance of physician functions, such as responsibility and commitment based on values, will increase, which will simultaneously increase the expectations of the patients that physicians will perform these functions. CONCLUSIONS We presented our findings on how the medical processes of physicians and patients will change as AI technology is fully implemented. Promoting interdisciplinary discussions on how to overcome the challenges is essential, referring to the discussions being conducted in other fields.
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Chalutz Ben-Gal H. Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study. Front Public Health 2023; 10:931225. [PMID: 36699881 PMCID: PMC9868720 DOI: 10.3389/fpubh.2022.931225] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023] Open
Abstract
Background Artificial intelligence (AI) is steadily entering and transforming the health care and Primary Care (PC) domains. AI-based applications assist physicians in disease detection, medical advice, triage, clinical decision-making, diagnostics and digital public health. Recent literature has explored physicians' perspectives on the potential impact of digital public health on key tasks in PC. However, limited attention has been given to patients' perspectives of AI acceptance in PC, specifically during the coronavirus pandemic. Addressing this research gap, we administered a pilot study to investigate criteria for patients' readiness to use AI-based PC applications by analyzing key factors affecting the adoption of digital public health technology. Methods The pilot study utilized a two-phase mixed methods approach. First, we conducted a qualitative study with 18 semi-structured interviews. Second, based on the Technology Readiness and Acceptance Model (TRAM), we conducted an online survey (n = 447). Results The results indicate that respondents who scored high on innovativeness had a higher level of readiness to use AI-based technology in PC during the coronavirus pandemic. Surprisingly, patients' health awareness and sociodemographic factors, such as age, gender and education, were not significant predictors of AI-based technology acceptance in PC. Conclusions This paper makes two major contributions. First, we highlight key social and behavioral determinants of acceptance of AI-enabled health care and PC applications. Second, we propose that to increase the usability of digital public health tools and accelerate patients' AI adoption, in complex digital public health care ecosystems, we call for implementing adaptive, population-specific promotions of AI technologies and applications.
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Wewetzer L, Held LA, Goetz K, Steinhäuser J. Determinants of the implementation of artificial intelligence-based screening for diabetic retinopathy-a cross-sectional study with general practitioners in Germany. Digit Health 2023; 9:20552076231176644. [PMID: 37274367 PMCID: PMC10233602 DOI: 10.1177/20552076231176644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 05/02/2023] [Indexed: 06/06/2023] Open
Abstract
Objective Diabetic retinopathy (DR) may lead to irreversible damage to the eye and cause blindness if diagnosed in its advanced stages. Artificial intelligence (AI) may support screening and contribute to a timely diagnosis. The aim of this study was to evaluate factors that might influence the success of implementing AI-supported devices for DR screenings in general practice. Methods A questionnaire with modules on attitudes toward digital solutions, technical factors, perceived patient perspectives, and sociodemographic data was constructed and 2100 general practitioners (GPs) in Germany were invited to participate via a personal letter. Results Two hundred nine physicians participated in the survey (10% response rate, mean age = 54 years, 46% women). Acquisition costs (mean = 1.37), remuneration (mean = 1.46), and running costs (mean = 1.40) were considered particularly relevant in the context of AI-based screening tools. GPs indicated that a mean of €27.00 (SD = 19) was considered to be an appropriate reimbursement for an AI-based screening for DR in their practice. Less relevant factors were availability of a smartphone used in the practice (mean = 2.53) and time until the examination result was available (mean = 2.29). Important technical factors were practicability of the device (mean = 1.27), unproblematic installation of any necessary software (mean = 1.34), and the integrability into the practice information system (mean = 1.44). Considering the patient welfare, physicians rated the accuracy of the examination, omission of pupil dilation, and the duration of the examination as the most important factors. Participants ranked the factors broadening the scope of care, strengthening the primary care (PC) range, and signs of modern medical practice as the most important factors for making an AI-based screening tool attractive for their practice. Conclusions These findings serve as a basis for a successful implementation of AI-assisted screening devices in PC and might facilitate early screenings for ophthalmological diseases in general practice. The most relevant barriers that need to be overcome for a successful implementation of such tools include clarification of the costs and reimbursement policies.
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Affiliation(s)
- Larisa Wewetzer
- Institute for Family Medicine, University Medical Center
Schleswig-Holstein, Lubeck Campus, Lubeck, Germany
| | - Linda A. Held
- Institute for Family Medicine, University Medical Center
Schleswig-Holstein, Lubeck Campus, Lubeck, Germany
| | - Katja Goetz
- Institute for Family Medicine, University Medical Center
Schleswig-Holstein, Lubeck Campus, Lubeck, Germany
| | - Jost Steinhäuser
- Institute for Family Medicine, University Medical Center
Schleswig-Holstein, Lubeck Campus, Lubeck, Germany
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25
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Tonon M, Moreau R. Using machine learning for predicting outcomes in ACLF. Liver Int 2022; 42:2354-2355. [PMID: 36162084 DOI: 10.1111/liv.15399] [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: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 01/22/2023]
Affiliation(s)
- Marta Tonon
- Unit of Internal Medicine and Hepatology, University Hospital of Padova, Padova, Italy
| | - Richard Moreau
- European Foundation for the Study of Chronic Liver Failure (EF CLIF), Barcelona, Spain.,INSERM, Université de Paris Cité, Centre de Recherche sur l'Inflammation (CRI), Paris, France.,Assistance Publique-Hôpitaux de Paris (AP-HP), and Hôpital Beaujon, Service d'Hépatologie, Clichy, France
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26
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Terry AL, Kueper JK, Beleno R, Brown JB, Cejic S, Dang J, Leger D, McKay S, Meredith L, Pinto AD, Ryan BL, Stewart M, Zwarenstein M, Lizotte DJ. Is primary health care ready for artificial intelligence? What do primary health care stakeholders say? BMC Med Inform Decis Mak 2022; 22:237. [PMID: 36085203 PMCID: PMC9461192 DOI: 10.1186/s12911-022-01984-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders.
Methods
This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews.
Results
Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality—denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don’t Matter: Just Another Tool in the Toolbox– reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword—the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care—broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care—elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation.
Conclusion
The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.
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27
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Kim MT, Heitkemper EM, Hébert ET, Hecht J, Crawford A, Nnaka T, Hutson TS, Rhee H, Radhakrishnan K. Redesigning culturally tailored intervention in the precision health era: Self-management science context. Nurs Outlook 2022; 70:710-724. [PMID: 35933178 PMCID: PMC9722518 DOI: 10.1016/j.outlook.2022.05.015] [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: 01/17/2022] [Revised: 05/18/2022] [Accepted: 05/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Nurse scientists have significantly contributed to health equity and ensuring cultural tailoring of interventions to meet unique needs of individuals. Methodologies for cultural tailoring of self-mangament interventions among marginalized populations have limitedly accommodated intersectionality and group heterogeneity when addressing health needs. PURPOSE Identify methodological limitations in cultural tailoring of interventions among priority populations and issue recommendations on cultural elements that researchers can target to ensure valid cultural tailoring approaches. METHODS Synthesis of literature on health equity, self-management, and implementation and dissemination research. FINDINGS Among priority populations, intersectionality and group heterogeneity has made group-based cultural tailoring approaches less effective in eliciting desirable health outcomes. Precision health methodology could be useful for cultural tailoring of interventions due to the methodology's focus on individual-level tailoring approaches. DISCUSSION We offer ways to advance health equity research using precision health approaches in cultural tailoring through targeting unique elements of culture and relevant psychosocial phenotypes.
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Affiliation(s)
- Miyong T Kim
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX.
| | - Elizabeth M Heitkemper
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Emily T Hébert
- The Center for Health Equity Research, Health Science Center at Houston, School of Publics Health Austin, The University of Texas, Austin, TX
| | - Jacklyn Hecht
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Alison Crawford
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Tonychris Nnaka
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Tara S Hutson
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Hyekyun Rhee
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
| | - Kavita Radhakrishnan
- The Center for Health Equity Research, School of Nursing, The University of Texas, Austin, TX
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28
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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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: 32] [Impact Index Per Article: 16.0] [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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Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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32
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Medical Informatics, the Internet, and Telemedicine. Fam Med 2022. [DOI: 10.1007/978-3-030-54441-6_51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Nilsen P, Svedberg P, Nygren J, Frideros M, Johansson J, Schueller S. Accelerating the impact of artificial intelligence in mental healthcare through implementation science. IMPLEMENTATION RESEARCH AND PRACTICE 2022; 3:26334895221112033. [PMID: 37091110 PMCID: PMC9924259 DOI: 10.1177/26334895221112033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. Methods The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Results Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Conclusion Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant. Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.
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Affiliation(s)
| | - Petra Svedberg
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | | | | | - Stephen Schueller
- Psychological Science, University of California Irvine, Irvine, CA, USA
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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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Glock H, Milos Nymberg V, Borgström Bolmsjö B, Holm J, Calling S, Wolff M, Pikkemaat M. Attitudes, Barriers, and Concerns Regarding Telemedicine Among Swedish Primary Care Physicians: A Qualitative Study. Int J Gen Med 2021; 14:9237-9246. [PMID: 34880663 PMCID: PMC8646113 DOI: 10.2147/ijgm.s334782] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/12/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose The primary care physician’s traditional patient contacts are challenged by the rapidly accelerating digital transformation. In a quantitative survey analysis based on the theory of planned behavior, we found high behavioral intention to use telemedicine among Swedish primary care physicians, but low reported use. The aim of this study was to further examine the physicians’ experiences regarding telemedicine, with a focus on possible explanations for the gap between intention and use, through analysis of the free-text comments supplied in the survey. Material and Methods The material was collected through a web-based survey which was sent out to physicians at 160 primary health care centers in southern Sweden from May to August 2019. The survey covered four areas: general experiences of telemedicine, digital contacts, chronic disease monitoring with digital tools, and artificial intelligence. A total of 100 physicians submitted one or more free-text comments. These were analyzed using qualitative content analysis with an inductive approach. Results The primary care physicians expressed attitudes towards telemedicine that focused on clinical usefulness. Barriers to use were the loss of personal contact with patients and a deficient technological infrastructure. The major concerns were that these factors would result in patient harm and an increased workload. The connection between intention and use postulated by the theory of planned behavior was not applicable in this context, as external factors in the form of availability and clinical usefulness of the specific technology were major impediments to use despite a generally positive attitude. Conclusion All telemedicine tools must be evaluated regarding clinical usefulness, patient safety, and effects on staff workload, and end users should be included in this process. Utmost consideration is needed regarding how to retain the benefits of personal contact between patient and provider when digital solutions are introduced.
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Affiliation(s)
- Hanna Glock
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Veronica Milos Nymberg
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Beata Borgström Bolmsjö
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Jonas Holm
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Susanna Calling
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Moa Wolff
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Miriam Pikkemaat
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
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Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun HTV, Chandavong S, Rheault N, T Wong S, Langlois L, Couturier Y, Salmeron JL, Gagnon MP, Légaré J. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. J Med Internet Res 2021; 23:e29839. [PMID: 34477556 PMCID: PMC8449300 DOI: 10.2196/29839] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. OBJECTIVE We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. METHODS We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. RESULTS We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). CONCLUSIONS We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.
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Affiliation(s)
- Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.,Mila-Quebec AI Institute, Montreal, QC, Canada
| | - France Légaré
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Gauri Sharma
- Faculty of Engineering, Dayalbagh Educational Institute, Agra, India
| | - Patrick Archambault
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Herve Tchala Vignon Zomahoun
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sam Chandavong
- Faculty of Science and Engineering, Université Laval, Quebec City, QC, Canada
| | - Nathalie Rheault
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sabrina T Wong
- School of Nursing, University of British Columbia, Vancouver, BC, Canada.,Center for Health Services and Policy Research, University of British Columbia, Vancouver, BC, Canada
| | - Lyse Langlois
- Department of Industrial Relations, Université Laval, Quebec City, QC, Canada.,OBVIA - Quebec International Observatory on the social impacts of AI and digital technology, Quebec City, QC, Canada
| | - Yves Couturier
- School of Social Work, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Jose L Salmeron
- Department of Data Science, University Pablo de Olavide, Seville, Spain
| | | | - Jean Légaré
- Arthritis Alliance of Canada, Montreal, QC, Canada
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Santos JC, Wong JHD, Pallath V, Ng KH. The perceptions of medical physicists towards relevance and impact of artificial intelligence. Phys Eng Sci Med 2021; 44:833-841. [PMID: 34283393 DOI: 10.1007/s13246-021-01036-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/13/2021] [Indexed: 01/04/2023]
Abstract
Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.
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Affiliation(s)
- Josilene C Santos
- Department of Nuclear Physics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Vinod Pallath
- Medical Education and Research Development Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
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Abstract
AbstractWe investigate expert disagreement over the potential and limitations of deep learning. We conducted 25 expert interviews to reveal the reasons and arguments that underlie the disagreement about the limitations of deep learning, here evaluated in respect to high-level machine intelligence. Experts in our sample named 40 limitations of deep learning. Using interview data, we identify and explore five crucial, unresolved research subjects that underpin this scholarly disagreement: abstraction, generalisation, explanatory models, emergence of planning and intervention. We suggest that such origins of disagreement can be used to form a research road map to guide efforts towards overcoming the limitations of deep learning.
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Roski J, Maier EJ, Vigilante K, Kane EA, Matheny ME. Enhancing trust in AI through industry self-governance. J Am Med Inform Assoc 2021; 28:1582-1590. [PMID: 33895824 PMCID: PMC8661431 DOI: 10.1093/jamia/ocab065] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 03/17/2020] [Accepted: 03/26/2021] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of enthusiasm for AI followed by periods of disillusionment, reduced investments, and progress, known as "AI Winters." We are now at risk of another AI Winter in health/healthcare due to increasing publicity of AI solutions that are not representing touted breakthroughs, and thereby decreasing trust of users in AI. In this article, we first highlight recently published literature on AI risks and mitigation strategies that would be relevant for groups considering designing, implementing, and promoting self-governance. We then describe a process for how a diverse group of stakeholders could develop and define standards for promoting trust, as well as AI risk-mitigating practices through greater industry self-governance. We also describe how adherence to such standards could be verified, specifically through certification/accreditation. Self-governance could be encouraged by governments to complement existing regulatory schema or legislative efforts to mitigate AI risks. Greater adoption of industry self-governance could fill a critical gap to construct a more comprehensive approach to the governance of AI solutions than US legislation/regulations currently encompass. In this more comprehensive approach, AI developers, AI users, and government/legislators all have critical roles to play to advance practices that maintain trust in AI and prevent another AI Winter.
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Affiliation(s)
| | | | | | | | - Michael E Matheny
- Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, Tennessee, USA
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Shinners L, Aggar C, Grace S, Smith S. Exploring healthcare professionals' perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method. Digit Health 2021; 7:20552076211003433. [PMID: 33815816 PMCID: PMC7995296 DOI: 10.1177/20552076211003433] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/23/2021] [Indexed: 01/15/2023] Open
Abstract
Objective The aim of this study was to draw upon the collective knowledge of experts in the fields of health and technology to develop a questionnaire that measured healthcare professionals' perceptions of Artificial Intelligence (AI). Methods The panel for this study were carefully selected participants who demonstrated an interest and/or involvement in AI from the fields of health or information technology. Recruitment was accomplished via email which invited the panel member to participate and included study and consent information. Data were collected from three rounds in the form of an online survey, an online group meeting and email communication. A 75% median threshold was used to define consensus. Results Between January and March 2019, five healthcare professionals and three IT experts participated in three rounds of study to reach consensus on the structure and content of the questionnaire. In Round 1 panel members identified issues about general understanding of AI and achieved consensus on nine draft questionnaire items. In Round 2 the panel achieved consensus on demographic questions and comprehensive group discussion resulted in the development of two further questionnaire items for inclusion. In a final e-Delphi round, a draft of the final questionnaire was distributed via email to the panel members for comment. No further amendments were put forward and 100% consensus was achieved. Conclusion A modified e-Delphi method was used to validate and develop a questionnaire to explore healthcare professionals' perceptions of AI. The e-Delphi method was successful in achieving consensus from an interdisciplinary panel of experts from health and IT. Further research is recommended to test the reliability of this questionnaire.
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Affiliation(s)
- Lucy Shinners
- Faculty of Health, Southern Cross University, Gold Coast Airport, Bilinga, Australia
| | - Christina Aggar
- Faculty of Health, Southern Cross University, Gold Coast Airport, Bilinga, Australia
| | - Sandra Grace
- Faculty of Health, Southern Cross University, East Lismore, Australia
| | - Stuart Smith
- Faculty of Health, Southern Cross University, Coffs Harbour, Australia
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Knop M, Weber S, Mueller M, Niehaves B. Human Factors and Technological Characteristics Influencing the Interaction with AI-enabled Clinical Decision Support Systems: A Literature Review (Preprint). JMIR Hum Factors 2021; 9:e28639. [PMID: 35323118 PMCID: PMC8990344 DOI: 10.2196/28639] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/02/2021] [Accepted: 02/07/2022] [Indexed: 01/22/2023] Open
Abstract
Background The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs. Objective Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS. Methods We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis. Results We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few (1/7, 14%) studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs. Conclusions Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered.
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Affiliation(s)
- Michael Knop
- Department of Information Systems, University of Siegen, Siegen, Germany
| | - Sebastian Weber
- Department of Information Systems, University of Siegen, Siegen, Germany
| | - Marius Mueller
- Department of Information Systems, University of Siegen, Siegen, Germany
| | - Bjoern Niehaves
- Department of Information Systems, University of Siegen, Siegen, Germany
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Liaw ST, Guo JGN, Ansari S, Jonnagaddala J, Godinho MA, Borelli AJ, de Lusignan S, Capurro D, Liyanage H, Bhattal N, Bennett V, Chan J, Kahn MG. Quality assessment of real-world data repositories across the data life cycle: A literature review. J Am Med Inform Assoc 2021; 28:1591-1599. [PMID: 33496785 DOI: 10.1093/jamia/ocaa340] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Data quality (DQ) must be consistently defined in context. The attributes, metadata, and context of longitudinal real-world data (RWD) have not been formalized for quality improvement across the data production and curation life cycle. We sought to complete a literature review on DQ assessment frameworks, indicators and tools for research, public health, service, and quality improvement across the data life cycle. MATERIALS AND METHODS The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases from health, physical and social sciences were used: Cinahl, Embase, Scopus, ProQuest, Emcare, PsycINFO, Compendex, and Inspec. Embase was used instead of PubMed (an interface to search MEDLINE) because it includes all MeSH (Medical Subject Headings) terms used and journals in MEDLINE as well as additional unique journals and conference abstracts. A combined data life cycle and quality framework guided the search of published and gray literature for DQ frameworks, indicators, and tools. At least 2 authors independently identified articles for inclusion and extracted and categorized DQ concepts and constructs. All authors discussed findings iteratively until consensus was reached. RESULTS The 120 included articles yielded concepts related to contextual (data source, custodian, and user) and technical (interoperability) factors across the data life cycle. Contextual DQ subcategories included relevance, usability, accessibility, timeliness, and trust. Well-tested computable DQ indicators and assessment tools were also found. CONCLUSIONS A DQ assessment framework that covers intrinsic, technical, and contextual categories across the data life cycle enables assessment and management of RWD repositories to ensure fitness for purpose. Balancing security, privacy, and FAIR principles requires trust and reciprocity, transparent governance, and organizational cultures that value good documentation.
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Affiliation(s)
- Siaw-Teng Liaw
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jason Guan Nan Guo
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Sameera Ansari
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jitendra Jonnagaddala
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Myron Anthony Godinho
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Alder Jose Borelli
- WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Capurro
- Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - Harshana Liyanage
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Navreet Bhattal
- Australian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia
| | - Vicki Bennett
- Australian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia
| | - Jaclyn Chan
- Australian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia
| | - Michael G Kahn
- Department of Pediatrics (Section of Informatics and Data Sciences), University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
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Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_340-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Goetz CM, Arnetz JE, Sudan S, Arnetz BB. Perceptions of virtual primary care physicians: A focus group study of medical and data science graduate students. PLoS One 2020; 15:e0243641. [PMID: 33332409 PMCID: PMC7745971 DOI: 10.1371/journal.pone.0243641] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/20/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Artificial and virtual technologies in healthcare have advanced rapidly, and healthcare systems have been adapting care accordingly. An intriguing new development is the virtual physician, which can diagnose and treat patients independently. METHODS AND FINDINGS This qualitative study of advanced degree students aimed to assess their perceptions of using a virtual primary care physician as a patient. Four focus groups were held: first year medical students, fourth year medical students, first year engineering/data science graduate students, and fourth year engineering/data science graduate students. The focus groups were audiotaped, transcribed verbatim, and content analyses of the transcripts was performed using a data-driven inductive approach. Themes identified concerned advantages, disadvantages, and the future of virtual primary care physicians. Within those main categories, 13 themes emerged and 31 sub-themes. DISCUSSION While participants appreciated that a virtual primary care physician would be convenient, efficient, and cost-effective, they also expressed concern about data privacy and the potential for misdiagnosis. To garner trust from its potential users, future virtual primary physicians should be programmed with a sufficient amount of trustworthy data and have a high level of transparency and accountability for patients.
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Affiliation(s)
- Courtney M. Goetz
- Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
| | - Judith E. Arnetz
- Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
| | - Sukhesh Sudan
- Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
| | - Bengt B. Arnetz
- Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
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Irfan F. Artificial Intelligence: Help or Hindrance for Family Physicians? Pak J Med Sci 2020; 37:288-291. [PMID: 33437293 PMCID: PMC7794111 DOI: 10.12669/pjms.37.1.3351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The use of Artificial Intelligence (AI) and related technologies is rapidly increasing and its application in clinical practice is a promising area of development. Artificial Intelligence can be a solution in the future as a physician’s new assistant; AI-physician combinations can act like models of ‘peaceful co-existence’. While it has the potential to mold many dimensions of patient care and can augment quality improvement, it cannot replace a family physician’s diagnostic intelligence, empathy and relationships. Physicians need to strike a balance between these combinations for better health outcomes without increasing patients’ frustration.
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Affiliation(s)
- Farhana Irfan
- Farhana Irfan King Saud University Chair for Medical Education Research and Development, Department of Family and Community Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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de Lusignan S, Liyanage H, McGagh D, Jani BD, Bauwens J, Byford R, Evans D, Fahey T, Greenhalgh T, Jones N, Mair FS, Okusi C, Parimalanathan V, Pell JP, Sherlock J, Tamburis O, Tripathy M, Ferreira F, Williams J, Hobbs FDR. COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology. JMIR Public Health Surveill 2020; 6:e21434. [PMID: 33112762 PMCID: PMC7674143 DOI: 10.2196/21434] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 02/06/2023] Open
Abstract
Background Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. Objective This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. Methods We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. Results Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). Conclusions The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.
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Affiliation(s)
- Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Harshana Liyanage
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Dylan McGagh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Bhautesh Dinesh Jani
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Jorgen Bauwens
- University Children's Hospital Basel, University of Basel, Basel, Switzerland
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Dai Evans
- PRIMIS, University of Nottingham, Nottingham, United Kingdom
| | - Tom Fahey
- Department of General Practice, Royal College of Surgeons, Ireland, Dublin, Ireland
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Nicholas Jones
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Frances S Mair
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Vaishnavi Parimalanathan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jill P Pell
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Julian Sherlock
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Oscar Tamburis
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Manasa Tripathy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Suvorova EI, Kontsevaya AV, Ryzhov AP, Myrzamatova AO, Mukaneeva DK, Khudyakov MB, Drapkina OM. Systematization of effective population-based preventive measures under uncertainty: an ontological approach. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2020. [DOI: 10.15829/1728-8800-2020-2505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- E. I. Suvorova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Kontsevaya
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. P. Ryzhov
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. O. Myrzamatova
- National Medical Research Center for Therapy and Preventive Medicine
| | - D. K. Mukaneeva
- National Medical Research Center for Therapy and Preventive Medicine
| | - M. B. Khudyakov
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Kocaballi AB, Ijaz K, Laranjo L, Quiroz JC, Rezazadegan D, Tong HL, Willcock S, Berkovsky S, Coiera E. Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners. J Am Med Inform Assoc 2020; 27:1695-1704. [PMID: 32845984 PMCID: PMC7671614 DOI: 10.1093/jamia/ocaa131] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/29/2020] [Accepted: 06/08/2020] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. MATERIALS AND METHODS Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. RESULTS Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. CONCLUSIONS AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.
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Affiliation(s)
- A Baki Kocaballi
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
- Faculty of Engineering & IT, University of Technology Sydney, Sydney, Australia
| | - Kiran Ijaz
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Liliana Laranjo
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Juan C Quiroz
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Dana Rezazadegan
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Victoria, Australia
| | - Huong Ly Tong
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Simon Willcock
- Health Sciences Centre, Macquarie University, Sydney, Australia
| | - Shlomo Berkovsky
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Enrico Coiera
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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Castagno S, Khalifa M. Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study. Front Artif Intell 2020; 3:578983. [PMID: 33733219 PMCID: PMC7861214 DOI: 10.3389/frai.2020.578983] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/22/2020] [Indexed: 01/16/2023] Open
Abstract
Objectives: The medical community is in agreement that artificial intelligence (AI) will have a radical impact on patient care in the near future. The purpose of this study is to assess the awareness of AI technologies among health professionals and to investigate their perceptions toward AI applications in medicine. Design: A web-based Google Forms survey was distributed via the Royal Free London NHS Foundation Trust e-newsletter. Setting: Only staff working at the NHS Foundation Trust received an invitation to complete the online questionnaire. Participants: 98 healthcare professionals out of 7,538 (response rate 1.3%; CI 95%; margin of error 9.64%) completed the survey, including medical doctors, nurses, therapists, managers, and others. Primary outcome: To investigate the prior knowledge of health professionals on the subject of AI as well as their attitudes and worries about its current and future applications. Results: 64% of respondents reported never coming across applications of AI in their work and 87% did not know the difference between machine learning and deep learning, although 50% knew at least one of the two terms. Furthermore, only 5% stated using speech recognition or transcription applications on a daily basis, while 63% never utilize them. 80% of participants believed there may be serious privacy issues associated with the use of AI and 40% considered AI to be potentially even more dangerous than nuclear weapons. However, 79% also believed AI could be useful or extremely useful in their field of work and only 10% were worried AI will replace them at their job. Conclusions: Despite agreeing on the usefulness of AI in the medical field, most health professionals lack a full understanding of the principles of AI and are worried about potential consequences of its widespread use in clinical practice. The cooperation of healthcare workers is crucial for the integration of AI into clinical practice and without it the NHS may miss out on an exceptionally rewarding opportunity. This highlights the need for better education and clear regulatory frameworks.
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Affiliation(s)
- Simone Castagno
- Department of Interventional Radiology, Royal Free Hospital, London, United Kingdom
| | - Mohamed Khalifa
- Department of Interventional Radiology, Royal Free Hospital, London, United Kingdom
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Blease C, Kharko A, Locher C, DesRoches CM, Mandl KD. US primary care in 2029: A Delphi survey on the impact of machine learning. PLoS One 2020; 15:e0239947. [PMID: 33031411 PMCID: PMC7544100 DOI: 10.1371/journal.pone.0239947] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 09/16/2020] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE To solicit leading health informaticians' predictions about the impact of AI/ML on primary care in the US in 2029. DESIGN A three-round online modified Delphi poll. PARTICIPANTS Twenty-nine leading health informaticians. METHODS In September 2019, health informatics experts were selected by the research team, and invited to participate the Delphi poll. Participation in each round was anonymous, and panelists were given between 4-8 weeks to respond to each round. In Round 1 open-ended questions solicited forecasts on the impact of AI/ML on: (1) patient care, (2) access to care, (3) the primary care workforce, (4) technological breakthroughs, and (5) the long-future for primary care physicians. Responses were coded to produce itemized statements. In Round 2, participants were invited to rate their agreement with each item along 7-point Likert scales. Responses were analyzed for consensus which was set at a predetermined interquartile range of ≤ 1. In Round 3 items that did not reach consensus were redistributed. RESULTS A total of 16 experts participated in Round 1 (16/29, 55%). Of these experts 13/16 (response rate, 81%), and 13/13 (response rate, 100%), responded to Rounds 2 and 3, respectively. As a result of developments in AI/ML by 2029 experts anticipated workplace changes including incursions into the disintermediation of physician expertise, and increased AI/ML training requirements for medical students. Informaticians also forecast that by 2029 AI/ML will increase diagnostic accuracy especially among those with limited access to experts, minorities and those with rare diseases. Expert panelists also predicted that AI/ML-tools would improve access to expert doctor knowledge. CONCLUSIONS This study presents timely information on informaticians' consensus views about the impact of AI/ML on US primary care in 2029. Preparation for the near-future of primary care will require improved levels of digital health literacy among patients and physicians.
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Affiliation(s)
- Charlotte Blease
- Division of General Medicine, OpenNotes, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Anna Kharko
- Department of Psychology, University of Plymouth, Plymouth, United Kingdom
| | - Cosima Locher
- Division of Clinical Psychology and Psychotherapy, University of Basel, Basel, Switzerland
- Department of Anesthesiology, Boston Children’s Hospital, Boston, MA, United States of America
| | - Catherine M. DesRoches
- Division of General Medicine, OpenNotes, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Kenneth D. Mandl
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States of America
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States of America
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