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Siira E, Tyskbo D, Nygren J. Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study. BMC PRIMARY CARE 2024; 25:268. [PMID: 39048973 PMCID: PMC11267767 DOI: 10.1186/s12875-024-02516-z] [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: 01/19/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare. METHODS We adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting. RESULTS The study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development. CONCLUSION The study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare.
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Affiliation(s)
- Elin Siira
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden
| | - Daniel Tyskbo
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden.
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2
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Moldt JA, Festl-Wietek T, Fuhl W, Zabel S, Claassen M, Wagner S, Nieselt K, Herrmann-Werner A. Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e58355. [PMID: 38989834 PMCID: PMC11238140 DOI: 10.2196/58355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/16/2024] [Accepted: 05/07/2024] [Indexed: 07/12/2024]
Abstract
Background The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.
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Affiliation(s)
- Julia-Astrid Moldt
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - Teresa Festl-Wietek
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - Wolfgang Fuhl
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Susanne Zabel
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Department of Internal Medicine, University Hospital of Tübingen, Tübingen, Germany
| | - Samuel Wagner
- Board of the Faculty of Medicine, University of Tübingen, Tübingen, Germany
| | - Kay Nieselt
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Anne Herrmann-Werner
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
- Department of Internal Medicine VI - Psychosomatic Medicine and Psychotherapy, University of Tübingen, Tübingen, Germany
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3
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Hasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics 2024; 25:55. [PMID: 38750441 PMCID: PMC11096093 DOI: 10.1186/s12910-024-01062-8] [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: 01/14/2024] [Accepted: 05/09/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges. METHODS A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests. RESULTS Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI. CONCLUSION The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.
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Affiliation(s)
- Hisham E Hasan
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan.
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan.
| | - Deema Jaber
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan
| | - Omar F Khabour
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, 22110, Jordan
| | - Karem H Alzoubi
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
- Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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Paiste HJ, Godwin RC, Smith AD, Berkowitz DE, Melvin RL. Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine. Front Digit Health 2024; 6:1316931. [PMID: 38444721 PMCID: PMC10912557 DOI: 10.3389/fdgth.2024.1316931] [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: 10/10/2023] [Accepted: 02/01/2024] [Indexed: 03/07/2024] Open
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.
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Affiliation(s)
- Henry J. Paiste
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Ryan C. Godwin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
- Department of Radiology, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Andrew D. Smith
- Department of Radiology, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Dan E. Berkowitz
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Ryan L. Melvin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
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6
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Fazakarley CA, Breen M, Leeson P, Thompson B, Williamson V. Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives. BMJ Open 2023; 13:e076950. [PMID: 38081671 PMCID: PMC10729128 DOI: 10.1136/bmjopen-2023-076950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS). DESIGN A qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out. SETTING NHS and UK higher education institutes. PARTICIPANTS Thirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings. RESULTS Four core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff. CONCLUSION This study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability. TRIAL REGISTRATION NUMBER NCT05028179; ISRCTN15113915; IRAS ref: 293515.
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Affiliation(s)
| | - Maria Breen
- School of Psychology & Clinical Language Sciences, University of Reading, Reading, UK
- Breen Clinical Research, London, UK
| | - Paul Leeson
- Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
| | | | - Victoria Williamson
- King's College London, London, UK
- Experimental Psychology, University of Oxford, Oxford, UK
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Vijayakumar S, Lee VV, Leong QY, Hong SJ, Blasiak A, Ho D. Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors 2023; 10:e48476. [PMID: 37902825 PMCID: PMC10644191 DOI: 10.2196/48476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/24/2023] [Accepted: 09/10/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice. OBJECTIVE This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. METHODS A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. RESULTS Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. CONCLUSIONS The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.
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Affiliation(s)
- Smrithi Vijayakumar
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - V Vien Lee
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Qiao Ying Leong
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Soo Jung Hong
- Department of Communications and New Media, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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8
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Ng FYC, Thirunavukarasu AJ, Cheng H, Tan TF, Gutierrez L, Lan Y, Ong JCL, Chong YS, Ngiam KY, Ho D, Wong TY, Kwek K, Doshi-Velez F, Lucey C, Coffman T, Ting DSW. Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Rep Med 2023; 4:101230. [PMID: 37852174 PMCID: PMC10591047 DOI: 10.1016/j.xcrm.2023.101230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/04/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023]
Abstract
Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.
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Affiliation(s)
- Faye Yu Ci Ng
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Arun James Thirunavukarasu
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; University of Cambridge School of Clinical Medicine, Cambridge, UK; Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Haoran Cheng
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Rollins School of Public Health, Emory University, Atlanta, GA, USA; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Ting Fang Tan
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore
| | - Yanyan Lan
- Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China
| | | | - Yap Seng Chong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Dean's Office, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kee Yuan Ngiam
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore; Insitute for Digital Medicine (WisDM), N.1 Institute for Health, National University of Singapore, Singapore, Singapore; Department of Pharmacology, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Kenneth Kwek
- Chief Executive Office, Singapore General Hospital, SingHealth, Singapore, Singapore
| | - Finale Doshi-Velez
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Catherine Lucey
- Executive Vice Chancellor and Provost Office, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas Coffman
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
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