Artificial intelligence tool development: what clinicians need to know? | BMC Medicine

Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. 2015;33:456–61.
Google Scholar
Tian S, Yang W, Grange JML, Wang P, Huang W, Ye Z. Smart healthcare: making medical care more intelligent. Glob Health J. 2019;3:62–5.
Google Scholar
Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, et al. Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis. NAM Perspect. 2022:10.31478/202209c.
Rosen R. How is technology changing clinician-patient relationships? BMJ. 2024;384:q574.
Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N. The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Med Inform Decis Mak. 2023;23:73.
Google Scholar
Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci. 2024;19:17.
Google Scholar
UNESCO. Ethical impact assessment: a tool of the Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO; 2023. Available from: Accessed 21 Apr 2025.
Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388:1201–8.
Google Scholar
U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. Silver Spring (MD): FDA; 2023. Available from: Accessed 21 Apr 2025.
Muehlematter UJ, Bluethgen C, Vokinger KN. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit Health. 2023;5:e618–26.
Google Scholar
Mehta N, Pandit A, Shukla S. Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study. J Biomed Inform. 2019;100: 103311.
Google Scholar
Bays HE, Fitch A, Cuda S, Gonsahn-Bollie S, Rickey E, Hablutzel J, et al. Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obes Pillars. 2023;6: 100065.
Google Scholar
Mohaideen K, Negi A, Verma DK, Kumar N, Sennimalai K, Negi A. Applications of artificial intelligence and machine learning in orthognathic surgery: A scoping review. J Stomatol Oral Maxillofac Surg. 2022;123:e962–72.
Google Scholar
Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res. 2021;23: e25759.
Google Scholar
Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Mousavi Baigi SF, Rezaei Sarsari M, Dahmardeh Kemmak F, et al. The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review. Health Sci Rep. 2024;7: e1893.
Google Scholar
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20:e262–73.
Google Scholar
Lysaght T, Lim HY, Xafis V, Ngiam KY. AI-Assisted Decision-making in Healthcare: The Application of an Ethics Framework for Big Data in Health and Research. Asian Bioeth Rev. 2019;11:299–314.
Google Scholar
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 25–60.
Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. Npj Digit Med. 2019;2:69.
Google Scholar
Bach TA, Kristiansen JK, Babic A, Jacovi A. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review. 2023. https://doi.org/10.48550/ARXIV.2310.03392.
Fehr J, Citro B, Malpani R, Lippert C, Madai VI. A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Front Digit Health. 2024;6:1267290.
Google Scholar
Yu K-H, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf. 2019;28:238–41.
Google Scholar
Adler-Milstein J, Redelmeier DA, Wachter RM. The Limits of Clinician Vigilance as an AI Safety Bulwark. JAMA. 2024. https://doi.org/10.1001/jama.2024.3620.
Google Scholar
Robertson C, Woods A, Bergstrand K, Findley J, Balser C, Slepian MJ. Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis. PLOS Digit Health. 2023;2: e0000237.
Google Scholar
Shaffer VA, Probst CA, Merkle EC, Arkes HR, Medow MA. Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System? Med Decis Making. 2013;33:108–18.
Google Scholar
Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research. Int J Med Inf. 2015;84:1009–18.
Google Scholar
Olakotan OO, Mohd YM. The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review. Health Informatics J. 2021;27:146045822110075.
Google Scholar
Kennedy G, Gallego B. Clinical prediction rules: A systematic review of healthcare provider opinions and preferences. Int J Med Inf. 2019;123:1–10.
Google Scholar
Knop M, Weber S, Mueller M, Niehaves B. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence-Enabled Clinical Decision Support Systems: Literature Review. JMIR Hum Factors. 2022;9: e28639.
Google Scholar
Artificial Intelligence (AI) in Healthcare Market (By Component: Software, Hardware, Services; By Application: Virtual Assistants, Diagnosis, Robot Assisted Surgery, Clinical Trials, Wearable, Others; By Technology: Machine Learning, Natural Language Processing, Context-aware Computing, Computer Vision; By End User) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2022 – 2030. Precedence Research; 2023.
Hassan N, Slight R, Morgan G, Bates DW, Gallier S, Sapey E, et al. Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making. BMJ Health Care Inform Online. 2023;30: e100784.
Google Scholar
Kwong JCC, Khondker A, Lajkosz K, McDermott MBA, Frigola XB, McCradden MD, et al. APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open. 2023;6: e2335377.
Google Scholar
Bakker L, Aarts J, Uyl-de Groot C, Redekop K. How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. BMC Med Inform Decis Mak. 2021;21:336.
Google Scholar
Committee on Human-System Integration Research Topics for the 711th Human Performance Wing of the Air Force Research Laboratory, Board on Human-Systems Integration, Division of Behavioral and Social Sciences and Education, National Academies of Sciences, Engineering, and Medicine. Human-AI Teaming: State-of-the-Art and Research Needs. Washington, D.C.: National Academies Press; 2022.
Tan M, Lee H, Wang D, Subramonyam H. Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education. 2023. https://doi.org/10.48550/ARXIV.2311.05792.
Ng FYC, Thirunavukarasu AJ, Cheng H, Tan TF, Gutierrez L, Lan Y, et al. Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Rep Med. 2023;4: 101230.
Google Scholar
Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26:1320–4.
Google Scholar
Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, et al. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health. 2022;4:e757–64.
Google Scholar
Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28:924–33.
Google Scholar
Cruz Rivera S, Liu X, Chan A-W, Denniston AK, Calvert MJ, The SPIRIT-AI and CONSORT-AI Working Group, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–63.
Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350 jan07 4:g7594–g7594.
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.
Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration. Ann Intern Med. 2019;170:W1.
Google Scholar
Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11: e048008.
Google Scholar
Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505.
Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11: e047709.
Google Scholar
Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc. 2020;27:2011–5.
Google Scholar
Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020;2: e200029.
Google Scholar
Hawksworth C, Elvidge J, Knies S, Zemplenyi A, Petykó Z, Siirtola P, et al. Protocol for the development of an artificial intelligence extension to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022. medRxiv. 2023. https://doi.org/10.1101/2023.05.31.23290788.
Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards for Interventions that use Artificial Intelligence (CHEERS-AI). Value Health. 2024;:S1098301524023660.
Bilbro NA, Hirst A, Paez A, Vasey B, Pufulete M, Sedrakyan A, et al. The IDEAL Reporting Guidelines: A Delphi Consensus Statement Stage Specific Recommendations for Reporting the Evaluation of Surgical Innovation. Ann Surg. 2021;273:82–5.
Google Scholar
McCulloch P, Altman DG, Campbell WB, Flum DR, Glasziou P, Marshall JC, et al. No surgical innovation without evaluation: the IDEAL recommendations. The Lancet. 2009;374:1105–12.
Google Scholar
Marcus HJ, Bennett A, Chari A, Day T, Hirst A, Hughes-Hallett A, et al. IDEAL-D Framework for Device Innovation: A Consensus Statement on the Preclinical Stage. Ann Surg. 2022;275:73–9.
Google Scholar
Lekadir K, Osuala R, Gallin C, Lazrak N, Kushibar K, Tsakou G, et al. FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. 2021. https://doi.org/10.48550/ARXIV.2109.09658.
Dagan N, Devons-Sberro S, Paz Z, Zoller L, Sommer A, Shaham G, et al. Evaluation of AI Solutions in Health Care Organizations — The OPTICA Tool. NEJM AI. 2024;1(1):e2300269.
European Commission. Ethics guidelines for trustworthy AI. Brussels: European Commission; 2019. Available from: Accessed 21 Apr 2025.
The STANDING Together collaboration. Recommendations for Diversity, Inclusivity, and Generalisability in Artificial Intelligence Health technologies and Health Datasets. 2023. Available from: https://zenodo.org/records/10048356.
Whicher D, Rapp T. The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned. Value Health. 2022;25:328–30.
Google Scholar
Simon GJ, Aliferis C, editors. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Cham: Springer International Publishing; 2024.
Das S, Nayak SP, Sahoo B, Nayak SC. Machine Learning in Healthcare Analytics: A State-of-the-Art Review. Arch Comput Methods Eng. 2024. https://doi.org/10.1007/s11831-024-10098-3.
Google Scholar
Mello MM, Guha N. Understanding Liability Risk from Using Health Care Artificial Intelligence Tools. N Engl J Med. 2024;390:271–8.
Google Scholar
Markowetz F. All models are wrong and yours are useless: making clinical prediction models impactful for patients. Npj Precis Oncol. 2024;8:54.
Google Scholar
Council of the European Union. Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts—analysis of the final compromise text with a view to agreement. Brussels: Council of the European Union; 2024. Available from: Accessed 21 Apr 2025.
Recommendation on the Ethics of Artificial Intelligence. programme and meeting document [184681]. France: UNESCO; 2022.
United Nations. Seizing the opportunities of safe, secure and trustworthy artificial intelligence systems for sustainable development. United Nations; 2024. Available from: Accessed 21 Apr 2025.
Biden Jr JR. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. 2023. Accessed 22 Apr 2024.
European Parliament. Artificial Intelligence Act. Brussels: European Parliament; 2024. Available from: https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
UNESCO. Readiness assessment methodology: a tool of the Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO; 2023. Available from: Accessed 21 Apr 2025.
UK Government. International scientific report on the safety of advanced AI: interim report. London: UK Government; 2024.
Josep SG, Sarah DN, Elias B, Ignacio S, Tatjana E, André A-A, et al. Harmonised Standards for the European AI Act. Seville: European Commission; 2024. Available from: Accessed 21 Apr 2025.
Ethics and governance of artificial intelligence for health. WHO guidance. Geneva: World Health Organization; 2021.
OECD AI. OECD AI Principles overview. Accessed 24 Apr 2024.
CAIDP. Center for AI and Digital Policy (CAIDP). Universal Guidelines for AI. 2018. Accessed 24 Apr 2024.
Asilomar AI Principles. Open Letters. 2017. Accessed 12 Oct 2024.
Ortega-Bolaños R, Bernal-Salcedo J, Germán Ortiz M, Galeano Sarmiento J, Ruz GA, Tabares-Soto R. Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems. Artif Intell Rev. 2024;57:110.
Google Scholar
Prainsack B, Forgó N. New AI regulation in the EU seeks to reduce risk without assessing public benefit. Nat Med. 2024. https://doi.org/10.1038/s41591-024-02874-2.
Google Scholar
Chaddad A, Peng J, Xu J, Bouridane A. Survey of Explainable AI Techniques in Healthcare. Sensors. 2023;23:634.
Google Scholar
Cohen IG, Evgeniou T, Gerke S, Minssen T. The European artificial intelligence strategy: implications and challenges for digital health. Lancet Digit Health. 2020;2:e376–9.
Google Scholar
Liu X, Glocker B, McCradden MM, Ghassemi M, Denniston AK, Oakden-Rayner L. The medical algorithmic audit. Lancet Digit Health. 2022;4:e384–97.
Google Scholar
Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLOS Med. 2018;15: e1002689.
Google Scholar
Klonoff DC. The Current Status of mHealth for Diabetes: Will it Be the Next Big Thing? J Diabetes Sci Technol. 2013;7:749–58.
Google Scholar
Zeitoun J-D, Ravaud P. Artificial intelligence in health care: value for whom? Lancet Digit Health. 2020;2:e338–9.
Google Scholar
Boulais W. Transforming Healthcare with AI: The NUHS Model and Its Global Implications. 2024. Accessed 5 Sep 2024.
Meet the doctor whose healthcare innovations are ‘out of this world.’ NUHS+ Health Inside Out. 2024. Accessed 6 Sep 2024.
Shorter hospital waiting times with artificial intelligence. NUHS+ Health Inside Out. 2023. Accessed 6 Sep 2024.
Chua CE, Lee Ying Clara N, Furqan MS, Lee Wai Kit J, Makmur A, Tham YC, et al. Integration of customised LLM for discharge summary generation in real-world clinical settings: a pilot study on RUSSELL GPT. Lancet Reg Health – West Pac. 2024;51:101211.
Pantuck AJ, Lee D, Kee T, Wang P, Lakhotia S, Silverman MH, et al. Modulating BET Bromodomain Inhibitor ZEN‐3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CURATE.AI, an Artificial Intelligence Platform. Adv Ther. 2018;1:1800104.
Blasiak A, Truong A, Tan WJ Lester, Kumar KS, Tan SB, Teo CB, et al. PRECISE CURATE.AI: A prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence. J Clin Oncol. 2022;40 16_suppl:1574–1574.
Blasiak A, Tan LWJ, Chong LM, Tadeo X, Truong ATL, Senthil Kumar K, et al. Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial. Npj Digit Med. 2024;7:223.
Zarrinpar A, Lee D-K, Silva A, Datta N, Kee T, Eriksen C, et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med. 2016;8.
Seyhan AA. Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. Transl Med Commun. 2019;4(1):18. https://doi.org/10.1186/s41231-019-0050-7.
Google Scholar
Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. https://doi.org/10.1136/bmj.m689.
Rajagopal A, Ayanian S, Ryu AJ, Qian R, Legler SR, Peeler EA, et al. Machine Learning Operations in Health Care: A Scoping Review. Mayo Clin Proc Digit Health. 2024;2:421–37.
Google Scholar
Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023. https://doi.org/10.7759/cureus.46454.
Google Scholar
Teo ZL, Kwee A, Lim JC, Lam CS, Ho D, Maurer-Stroh S, et al. Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside. Ann Acad Med Singapore. 2023;52:199–212.
Google Scholar
Ayorinde A, Mensah DO, Walsh J, Ghosh I, Ibrahim SA, Hogg J, et al. Health Care Professionals’ Experience of Using AI: Systematic Review With Narrative Synthesis. J Med Internet Res. 2024;26: e55766.
Google Scholar
Celi LA, Cellini J, Charpignon M-L, Dee EC, Dernoncourt F, Eber R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digit Health. 2022;1: e0000022.
Google Scholar
Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21(2):345–52. https://doi.org/10.1093/biostatistics/kxz041.
Ganapathi S, Palmer J, Alderman JE, Calvert M, Espinoza C, Gath J, et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat Med. 2022;28:2232–3.
Google Scholar
Ratwani RM, Sutton K, Galarraga JE. Addressing AI Algorithmic Bias in Health Care. JAMA. 2024;332:1051.
Google Scholar
Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. J Am Med Inform Assoc. 2024;31:1172–83.
Google Scholar
Ghebrehiwet I, Zaki N, Damseh R, Mohamad MS. Revolutionizing personalized medicine with generative AI: a systematic review. Artif Intell Rev. 2024;57:128.
Google Scholar
Ibrahim M, Khalil YA, Amirrajab S, Sun C, Breeuwer M, Pluim J, et al. Generative AI for synthetic data across multiple medical modalities: a systematic review of recent developments and challenges. arXiv. 2024. https://doi.org/10.48550/arXiv.2407.00116.
Takita H, Kabata D, Walston SL, Tatekawa H, Saito K, Tsujimoto Y, et al. Diagnostic performance comparison between generative AI and physicians: a systematic review and meta-analysis. medRxiv. 2024. https://doi.org/10.1101/2024.01.20.24301563.
Beam AL, Manrai AK, Ghassemi M. Challenges to the Reproducibility of Machine Learning Models in Health Care. JAMA. 2020;323:305.
Google Scholar
Azad TD, Ehresman J, Ahmed AK, Staartjes VE, Lubelski D, Stienen MN, et al. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. Spine J. 2021;21:1610–6.
Google Scholar
Alberto IRI, Alberto NRI, Altinel Y, Blacker S, Binotti WW, Celi LA, et al. A scientometric analysis of fairness in health AI literature. PLOS Glob Public Health. 2024;4: e0002513.
Google Scholar
Yang R, Nair SV, Ke Y, D’Agostino D, Liu M, Ning Y, et al. Disparities in clinical studies of AI enabled applications from a global perspective. Npj Digit Med. 2024;7:209.
Google Scholar
Serra-Burriel M, Locher L, Development VKN, Pipeline and Geographic Representation of trials for artificial intelligence, machine learning-enabled medical devices (2010 to 2023). NEJM AI. 2024;1(1):AIpc2300038.
Liu M, Ning Y, Teixayavong S, Mertens M, Xu J, Ting DSW, et al. A translational perspective towards clinical AI fairness. Npj Digit Med. 2023;6:172.
Google Scholar
Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc. 2023;30:2050–63.
Google Scholar
Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. A lesson in implementation: A pre-post study of providers’ experience with artificial intelligence-based clinical decision support. Int J Med Inf. 2020;137: 104072.
Google Scholar
Van De Sande D, Van Genderen ME, Smit JM, Huiskens J, Visser JJ, Veen RER, et al. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform. 2022;29: e100495.
Google Scholar
Katharine M. Should AI Models Be Explainable? That depends. 2021. Accessed 20 Mar 2024.
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28:31–8.
Google Scholar
Stiefel KM, Coggan JS. The energy challenges of artificial superintelligence. Front Artif Intell. 2023;6:1240653.
Google Scholar
Jovanovic M, Mitrov G, Zdravevski E, Lameski P, Colantonio S, Kampel M, et al. Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns. J Med Internet Res. 2022;24: e36553.
Google Scholar
Wolff J, Pauling J, Keck A, Baumbach J. Systematic Review of Economic Impact Studies of Artificial Intelligence in Health Care. J Med Internet Res. 2020;22: e16866.
Google Scholar
Wolff J, Pauling J, Keck A, Baumbach J. Success Factors of Artificial Intelligence Implementation in Healthcare. Front Digit Health. 2021;3: 594971.
Google Scholar
Saenz AD, Mass General Brigham AI Governance Committee, McCoy T, Mantha AB, Martin R, Damiano R, et al. Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions. Npj Digit Med. 2024;7:348.
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