{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T02:48:17Z","timestamp":1784688497021,"version":"3.55.0"},"reference-count":129,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005969","name":"Universit\u00e0 di Bologna","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005969","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Minds & Machines"],"DOI":"10.1007\/s11023-024-09692-y","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T03:30:51Z","timestamp":1726025451000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality"],"prefix":"10.1007","volume":"34","author":[{"given":"Kassandra","family":"Karpathakis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5221-4770","authenticated-orcid":false,"given":"Jessica","family":"Morley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luciano","family":"Floridi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"9692_CR1","unstructured":"\u00a321 million to roll out artificial intelligence across the NHS. Press release. GOV.UK. June 23, 2023. Accessed January 15, 2024. https:\/\/www.gov.uk\/government\/news\/21-million-to-roll-out-artificial-intelligence-across-the-nhs"},{"key":"9692_CR3","doi-asserted-by":"publisher","first-page":"e43110","DOI":"10.2196\/43110","volume":"25","author":"LC Adams","year":"2023","unstructured":"Adams, L. C., Busch, F., Truhn, D., Makowski, M. R., Aerts, H. J. W. L., & Bressem, K. K. (2023). What does DALL-E 2 know about radiology? Journal of Medical Internet Research, 25, e43110. https:\/\/doi.org\/10.2196\/43110","journal-title":"Journal of Medical Internet Research"},{"key":"9692_CR4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ijmedinf.2017.12.024","volume":"111","author":"N Afzal","year":"2018","unstructured":"Afzal, N., Mallipeddi, V. P., Sohn, S., et al. (2018). Natural language processing of clinical notes for identification of critical limb ischemia. International Journal of Medical Informatics, 111, 83\u201389. https:\/\/doi.org\/10.1016\/j.ijmedinf.2017.12.024","journal-title":"International Journal of Medical Informatics"},{"key":"9692_CR5","unstructured":"Agency for Electronic Government and Information and Knowledge Society. Artificial intelligence strategy for the digital government. AGESIC; 2019. Accessed January 14, 2024. https:\/\/wp.oecd.ai\/app\/uploads\/2021\/12\/Uruguay_Artificial_Intelligence_Strategy_for_Digital_Government_2019.pdf"},{"issue":"7","key":"9692_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/17707","volume":"22","author":"H Alami","year":"2020","unstructured":"Alami, H., Lehoux, P., Auclair, Y., et al. (2020). Artificial intelligence and health technology assessment: Anticipating a new level of complexity. Journal of Medical Internet Research, 22(7), e17707. https:\/\/doi.org\/10.2196\/17707","journal-title":"Journal of Medical Internet Research"},{"key":"9692_CR7","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","volume":"96","author":"AS Albahri","year":"2023","unstructured":"Albahri, A. S., Duhaim, A. M., Fadhel, M. A., et al. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion., 96, 156\u2013191. https:\/\/doi.org\/10.1016\/j.inffus.2023.03.008","journal-title":"Information Fusion."},{"issue":"1","key":"9692_CR8","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1186\/s12889-021-10630-1","volume":"21","author":"H Alderwick","year":"2021","unstructured":"Alderwick, H., Hutchings, A., Briggs, A., & Mays, N. (2021). The impacts of collaboration between local health care and non-health care organizations and factors shaping how they work: A systematic review of reviews. BMC Public Health, 21(1), 753. https:\/\/doi.org\/10.1186\/s12889-021-10630-1","journal-title":"BMC Public Health"},{"issue":"11","key":"9692_CR9","doi-asserted-by":"publisher","first-page":"4153","DOI":"10.1111\/jocs.15934","volume":"36","author":"R Aleman","year":"2021","unstructured":"Aleman, R., Patel, S., Sleiman, J., Navia, J., Sheffield, C., & Brozzi, N. A. (2021). Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares. Journal of Cardiac Surgery, 36(11), 4153\u20134159. https:\/\/doi.org\/10.1111\/jocs.15934","journal-title":"Journal of Cardiac Surgery"},{"issue":"7","key":"9692_CR10","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1377\/hlthaff.2014.0352","volume":"33","author":"R Amarasingham","year":"2014","unstructured":"Amarasingham, R., Patzer, R. E., Huesch, M., Nguyen, N. Q., & Xie, B. (2014). Implementing electronic health care predictive analytics: Considerations and challenges. Health Affairs., 33(7), 1148\u20131154. https:\/\/doi.org\/10.1377\/hlthaff.2014.0352","journal-title":"Health Affairs."},{"issue":"10","key":"9692_CR11","doi-asserted-by":"publisher","DOI":"10.2196\/21621","volume":"8","author":"SM Araujo","year":"2020","unstructured":"Araujo, S. M., Sousa, P., & Dutra, I. (2020). Clinical decision support systems for pressure ulcer management: Systematic review. JMIR Medical Informatics, 8(10), e21621. https:\/\/doi.org\/10.2196\/21621","journal-title":"JMIR Medical Informatics"},{"key":"9692_CR12","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.k156","volume":"360","author":"S Armstrong","year":"2018","unstructured":"Armstrong, S. (2018). The apps attempting to transfer NHS 111 online. BMJ, 360, k156. https:\/\/doi.org\/10.1136\/bmj.k156","journal-title":"BMJ"},{"issue":"2","key":"9692_CR13","doi-asserted-by":"publisher","first-page":"190","DOI":"10.7861\/fhj.2022-0013","volume":"9","author":"A Arora","year":"2022","unstructured":"Arora, A., & Arora, A. (2022). Generative adversarial networks and synthetic patient data: Current challenges and future perspectives. Future Healthcare Journal, 9(2), 190. https:\/\/doi.org\/10.7861\/fhj.2022-0013","journal-title":"Future Healthcare Journal"},{"issue":"10201","key":"9692_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","volume":"394","author":"ZI Attia","year":"2019","unstructured":"Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., et al. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet, 394(10201), 861\u2013867. https:\/\/doi.org\/10.1016\/S0140-6736(19)31721-0","journal-title":"Lancet"},{"issue":"1","key":"9692_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/bmb\/ldab016","volume":"139","author":"YYM Aung","year":"2021","unstructured":"Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021). The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4\u201315. https:\/\/doi.org\/10.1093\/bmb\/ldab016","journal-title":"British Medical Bulletin"},{"key":"9692_CR16","unstructured":"Australian Digital Health Agency. Australia\u2019s national digital health strategy: safe, seamless and secure: Evolvign health and care to meet the needs of modern Australia. Australian Digital Health Agency; 2018. Accessed January 14, 2024. https:\/\/www.digitalhealth.gov.au\/sites\/default\/files\/2020-11\/Australia%27s%20National%20Digital%20Health%20Strategy%20-%20Safe%2C%20seamless%20and%20secure.pdf"},{"issue":"5","key":"9692_CR17","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1136\/bmjebm-2018-110953","volume":"23","author":"A Banerjee","year":"2018","unstructured":"Banerjee, A., Drumright, L. N., & Mitchell, A. R. (2018). Can the NHS be a learning healthcare system in the age of digital technology? BMJ Evidence-Based Medicine, 23(5), 161\u2013165. https:\/\/doi.org\/10.1136\/bmjebm-2018-110953","journal-title":"BMJ Evidence-Based Medicine"},{"issue":"1","key":"9692_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/obhd.1997.2690","volume":"70","author":"J Baron","year":"1997","unstructured":"Baron, J., & Spranca, M. (1997). Protected values. Organizational Behavior and Human Decision Processes., 70(1), 1\u201316. https:\/\/doi.org\/10.1006\/obhd.1997.2690","journal-title":"Organizational Behavior and Human Decision Processes."},{"key":"9692_CR19","doi-asserted-by":"publisher","first-page":"S137","DOI":"10.7326\/M19-0872","volume":"172","author":"DW Bates","year":"2020","unstructured":"Bates, D. W., Auerbach, A., Schulam, P., Wright, A., & Saria, S. (2020). Reporting and implementing interventions involving machine learning and artificial intelligence. Annals of Internal Medicine., 172, S137\u2013S144. https:\/\/doi.org\/10.7326\/M19-0872","journal-title":"Annals of Internal Medicine."},{"key":"9692_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.736697","volume":"4","author":"JC B\u00e9lisle-Pipon","year":"2021","unstructured":"B\u00e9lisle-Pipon, J. C., Couture, V., Roy, M. C., Ganache, I., Goetghebeur, M., & Cohen, I. G. (2021). What makes artificial intelligence exceptional in health technology assessment? Frontiers in Artificial Intelligence, 4, e736697. https:\/\/doi.org\/10.3389\/frai.2021.736697","journal-title":"Frontiers in Artificial Intelligence"},{"key":"9692_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101785","volume":"103","author":"D Ben-Israel","year":"2020","unstructured":"Ben-Israel, D., Jacobs, W. B., Casha, S., et al. (2020). The impact of machine learning on patient care: A systematic review. Artificial Intelligence in Medicine, 103, e101785. https:\/\/doi.org\/10.1016\/j.artmed.2019.101785","journal-title":"Artificial Intelligence in Medicine"},{"issue":"1","key":"9692_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00324-0","volume":"3","author":"S Benjamens","year":"2020","unstructured":"Benjamens, S., Dhunnoo, P., & Mesk\u00f3, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine., 3(1), e118. https:\/\/doi.org\/10.1038\/s41746-020-00324-0","journal-title":"NPJ Digital Medicine."},{"key":"9692_CR55","unstructured":"Bloomberg Intelligence. Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds. Bloomberg L.P. June 1, 2023. Accessed January 14, 2024. https:\/\/www.bloomberg.com\/company\/press\/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds\/"},{"issue":"21","key":"9692_CR23","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1056\/NEJMp0900702","volume":"360","author":"JS Brownstein","year":"2009","unstructured":"Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009). Digital disease detection\u2014Harnessing the web for public health surveillance. New England Journal of Medicine, 360(21), 2153\u20132157. https:\/\/doi.org\/10.1056\/NEJMp0900702","journal-title":"New England Journal of Medicine"},{"issue":"5","key":"9692_CR24","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1016\/j.amjsurg.2020.06.073","volume":"220","author":"QD Buchlak","year":"2020","unstructured":"Buchlak, Q. D., Esmaili, N., Leveque, J. C., Bennett, C., Piccardi, M., & Farrokhi, F. (2020). Ethical thinking machines in surgery and the requirement for clinical leadership. The American Journal of Surgery., 220(5), 1372\u20131374. https:\/\/doi.org\/10.1016\/j.amjsurg.2020.06.073","journal-title":"The American Journal of Surgery."},{"key":"9692_CR25","unstructured":"Cavazzoni P. FDA Releases Two Discussion Papers to Spur Conversation about Artificial Intelligence and Machine Learning in Drug Development and Manufacturing. FDA. May 10, 2023. Accessed January 14, 2024. https:\/\/www.fda.gov\/news-events\/fda-voices\/fda-releases-two-discussion-papers-spur-conversation-about-artificial-intelligence-and-machine"},{"issue":"6","key":"9692_CR26","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","volume":"5","author":"RJ Chen","year":"2021","unstructured":"Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021). Synthetic data in machine learning for medicine and healthcare. Nature Biomedical Engineering., 5(6), 493\u2013497. https:\/\/doi.org\/10.1038\/s41551-021-00751-8","journal-title":"Nature Biomedical Engineering."},{"issue":"10","key":"9692_CR27","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1093\/jamia\/ocac110","volume":"29","author":"W Chen","year":"2022","unstructured":"Chen, W., Howard, K., Gorham, G., et al. (2022). Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: A systematic review and meta-analysis. Journal of the American Medical Informatics Association, 29(10), 1757\u20131772. https:\/\/doi.org\/10.1093\/jamia\/ocac110","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"1","key":"9692_CR28","doi-asserted-by":"publisher","first-page":"24454","DOI":"10.1038\/srep24454","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng, J. Z., Ni, D., Chou, Y. H., et al. (2016). Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Science and Reports, 6(1), 24454. https:\/\/doi.org\/10.1038\/srep24454","journal-title":"Science and Reports"},{"issue":"2","key":"9692_CR29","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1353\/pbm.2019.0012","volume":"62","author":"B Chin-Yee","year":"2019","unstructured":"Chin-Yee, B., & Upshur, R. (2019). Three problems with big data and artificial intelligence in medicine. Perspectives in Biology and Medicine., 62(2), 237\u2013256. https:\/\/doi.org\/10.1353\/pbm.2019.0012","journal-title":"Perspectives in Biology and Medicine."},{"key":"9692_CR30","unstructured":"CIFAR. AICan 2020: CIFAR Pan-Canadian AI Strategy Impact Report. CIFAR; 2020. Accessed January 14, 2024. https:\/\/cifar.ca\/wp-content\/uploads\/2020\/11\/AICan-2020-CIFAR-Pan-Canadian-AI-Strategy-Impact-Report.pdf"},{"key":"9692_CR31","doi-asserted-by":"publisher","first-page":"117959721985656","DOI":"10.1177\/1179597219856564","volume":"10","author":"CV Cosgriff","year":"2019","unstructured":"Cosgriff, C. V., Celi, L. A., & Stone, D. J. (2019). Critical care, critical data. Biomedical Engineering and Computational Biology, 10, 117959721985656. https:\/\/doi.org\/10.1177\/1179597219856564","journal-title":"Biomedical Engineering and Computational Biology"},{"key":"9692_CR32","first-page":"76","volume":"11","author":"L Dadayan","year":"2008","unstructured":"Dadayan, L. (2008). Measuring return on government IT investments. Communications in Dependability and Quality Management (CDQM)., 11, 76\u201386.","journal-title":"Communications in Dependability and Quality Management (CDQM)."},{"key":"9692_CR33","unstructured":"Department of Health & Social Care. Deliverable 2: Principles to Support the Development and Deployment of Artificial Intelligence or Machine Learning-Enabled Medical Devices across Jurisdictions. UK Government; 2021. Accessed January 16, 2024. https:\/\/www.gov.uk\/government\/publications\/g7-health-track-digital-health-final-reports\/deliverable-2-principles-to-support-the-development-and-deployment-of-artificial-intelligence-or-machine-learning-enabled-medical-devices-across-juri"},{"key":"9692_CR34","unstructured":"Department of Health & Social Care. A Plan for Digital Health and Social Care. GOV.UK; 2022. Accessed January 14, 2024. https:\/\/www.gov.uk\/government\/publications\/a-plan-for-digital-health-and-social-care\/a-plan-for-digital-health-and-social-care"},{"key":"9692_CR105","unstructured":"Department of Industry, Science & Resources. Australia\u2019s Artificial Intelligence Action Plan. Australian Government; 2021. Accessed January 14, 2024. https:\/\/webarchive.nla.gov.au\/awa\/20220816053410\/https:\/\/www.industry.gov.au\/data-and-publications\/australias-artificial-intelligence-action-plan"},{"key":"9692_CR35","unstructured":"Dingel H, Wagner E, Rakshit S, et al. The state of the U.S. health system in 2022 and the outlook for 2023. Peterson-KFF Health System Tracker. December 22, 2022. Accessed January 14, 2024. https:\/\/www.healthsystemtracker.org\/brief\/the-state-of-the-u-s-health-system-in-2022-and-the-outlook-for-2023\/"},{"issue":"1","key":"9692_CR37","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1177\/135581960501000110","volume":"10","author":"M Dixon-Woods","year":"2005","unstructured":"Dixon-Woods, M., Agarwal, S., Jones, D., Young, B., & Sutton, A. (2005). Synthesising qualitative and quantitative evidence: A review of possible methods. Journal of Health Services Research & Policy, 10(1), 45\u201353. https:\/\/doi.org\/10.1177\/135581960501000110","journal-title":"Journal of Health Services Research & Policy"},{"issue":"3","key":"9692_CR38","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1136\/qshc.1.3.178","volume":"1","author":"L Doyal","year":"1992","unstructured":"Doyal, L. (1992). Need for moral audit in evaluating quality in health care. Quality and Safety in Health Care., 1(3), 178\u2013183. https:\/\/doi.org\/10.1136\/qshc.1.3.178","journal-title":"Quality and Safety in Health Care."},{"issue":"23","key":"9692_CR39","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1001\/jama.2019.4914","volume":"321","author":"EJ Emanuel","year":"2019","unstructured":"Emanuel, E. J., & Wachter, R. M. (2019). Artificial intelligence in health care: Will the value match the hype? JAMA, 321(23), 2281\u20132282. https:\/\/doi.org\/10.1001\/jama.2019.4914","journal-title":"JAMA"},{"key":"9692_CR94","unstructured":"European Parliament. EU AI Act: first regulation on artificial intelligence. News European Parliament. December 19, 2023. Accessed January 14, 2024. https:\/\/www.europarl.europa.eu\/news\/en\/headlines\/society\/20230601STO93804\/eu-ai-act-first-regulation-on-artificial-intelligence"},{"key":"9692_CR40","doi-asserted-by":"publisher","DOI":"10.2196\/46885","volume":"9","author":"G Eysenbach","year":"2023","unstructured":"Eysenbach, G. (2023). The role of ChatGPT, generative language models, and artificial intelligence in medical education: A conversation with ChatGPT and a call for papers. JMIR Medical Education., 9, e46885. https:\/\/doi.org\/10.2196\/46885","journal-title":"JMIR Medical Education."},{"key":"9692_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102524","volume":"139","author":"W Ge","year":"2023","unstructured":"Ge, W., Lueck, C., Suominen, H., & Apthorp, D. (2023). Has machine learning over-promised in healthcare?: A critical analysis and a proposal for improved evaluation, with evidence from Parkinson\u2019s disease. Artificial Intelligence in Medicine, 139, e102524. https:\/\/doi.org\/10.1016\/j.artmed.2023.102524","journal-title":"Artificial Intelligence in Medicine"},{"key":"9692_CR42","unstructured":"GenerativeAI. All things generative AI: first, what is generative AI? GENERATIVE AI. Accessed January 14, 2024. https:\/\/generativeai.net\/"},{"key":"9692_CR43","volume-title":"The discovery of grounded theory: Strategies for qualitative research","author":"BG Glaser","year":"1967","unstructured":"Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Sociology Press."},{"key":"9692_CR44","unstructured":"GlobeNewswire. Generative AI in healthcare market set to reach a valuation of USD 17.2 Bn by 2032 | Data analysis by experts at Market.us. GlobeNewswire News Room. https:\/\/www.globenewswire.com\/news-release\/2023\/04\/03\/2639957\/0\/en\/Generative-AI-in-Healthcare-Market-Set-to-Reach-a-Valuation-of-USD-17-2-Bn-by-2032-Data-Analysis-by-Experts-at-Market-us.html. September 18, 2023. Accessed January 14, 2024."},{"key":"9692_CR45","unstructured":"Grand View Research. Artificial Intelligence market size, share & trends analysis report by solution, by technology (deep learning, machine learning), by end-user, by region, and segment forecasts, 2023\u20132030. Grand View Research; 2023. Accessed January 14, 2024. https:\/\/www.grandviewresearch.com\/industry-analysis\/artificial-intelligence-ai-market"},{"issue":"613","key":"9692_CR46","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3399\/bjgp13X670741","volume":"63","author":"T Greenhalgh","year":"2013","unstructured":"Greenhalgh, T. (2013). Five biases of new technologies. British Journal of General Practice, 63(613), 425\u2013425. https:\/\/doi.org\/10.3399\/bjgp13X670741","journal-title":"British Journal of General Practice"},{"issue":"4","key":"9692_CR47","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1111\/j.0887-378X.2004.00325.x","volume":"82","author":"T Greenhalgh","year":"2004","unstructured":"Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Quarterly., 82(4), 581\u2013629. https:\/\/doi.org\/10.1111\/j.0887-378X.2004.00325.x","journal-title":"Milbank Quarterly."},{"key":"9692_CR48","doi-asserted-by":"publisher","first-page":"104512","DOI":"10.1016\/j.ebiom.2023.104512","volume":"90","author":"S Harrer","year":"2023","unstructured":"Harrer, S. (2023). Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. eBioMedicine, 90, 104512. https:\/\/doi.org\/10.1016\/j.ebiom.2023.104512","journal-title":"eBioMedicine"},{"issue":"1","key":"9692_CR49","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.acra.2019.09.017","volume":"27","author":"HB Harvey","year":"2020","unstructured":"Harvey, H. B., & Gowda, V. (2020). How the FDA regulates AI. Academic Radiology, 27(1), 58\u201361. https:\/\/doi.org\/10.1016\/j.acra.2019.09.017","journal-title":"Academic Radiology"},{"issue":"1","key":"9692_CR50","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","volume":"25","author":"J He","year":"2019","unstructured":"He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30\u201336. https:\/\/doi.org\/10.1038\/s41591-018-0307-0","journal-title":"Nature Medicine"},{"issue":"1","key":"9692_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3325\/cmj.2023.64.1","volume":"64","author":"J Homolak","year":"2023","unstructured":"Homolak, J. (2023). Opportunities and risks of ChatGPT in medicine, science, and academic publishing: A modern Promethean dilemma. Croatian Medical Journal, 64(1), 1\u20133. https:\/\/doi.org\/10.3325\/cmj.2023.64.1","journal-title":"Croatian Medical Journal"},{"issue":"5\u20136","key":"9692_CR52","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1159\/000504785","volume":"22","author":"D Horgan","year":"2019","unstructured":"Horgan, D., Romao, M., Morr\u00e9, S. A., & Kalra, D. (2019). Artificial intelligence: Power for civilisation\u2014And for better healthcare. Public Health Genomics, 22(5\u20136), 145\u2013161. https:\/\/doi.org\/10.1159\/000504785","journal-title":"Public Health Genomics"},{"issue":"23","key":"9692_CR53","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1001\/jama.2019.16842","volume":"322","author":"TJ Hwang","year":"2019","unstructured":"Hwang, T. J., Kesselheim, A. S., & Vokinger, K. N. (2019). Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA, 322(23), 2285. https:\/\/doi.org\/10.1001\/jama.2019.16842","journal-title":"JAMA"},{"key":"9692_CR54","unstructured":"Innovation, Science & Economic Development Canada. Government of Canada invests in responsible artificial intelligence research at the Universit\u00e9 de Montr\u00e9al. Canada.ca. June 9, 2023. Accessed January 15, 2024. https:\/\/www.canada.ca\/en\/innovation-science-economic-development\/news\/2023\/06\/government-of-canada-invests-in-responsible-artificial-intelligence-research-at-the-universite-de-montreal.html"},{"issue":"1","key":"9692_CR56","doi-asserted-by":"publisher","DOI":"10.1186\/s12935-021-01981-1","volume":"21","author":"MJ Iqbal","year":"2021","unstructured":"Iqbal, M. J., Javed, Z., Sadia, H., et al. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future. Cancer Cell International, 21(1), e270. https:\/\/doi.org\/10.1186\/s12935-021-01981-1","journal-title":"Cancer Cell International"},{"key":"9692_CR57","unstructured":"Italian Government. Strategic Programme on Artificial Intelligence 2022\u20132024. Italian Government; 2021. Accessed January 14, 2024. https:\/\/assets.innovazione.gov.it\/1637777513-strategic-program-aiweb.pdf"},{"key":"9692_CR59","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.ijmedinf.2016.09.010","volume":"97","author":"KH Jones","year":"2017","unstructured":"Jones, K. H., Laurie, G., Stevens, L., Dobbs, C., Ford, D. V., & Lea, N. (2017). The other side of the coin: Harm due to the non-use of health-related data. International Journal of Medical Informatics., 97, 43\u201351. https:\/\/doi.org\/10.1016\/j.ijmedinf.2016.09.010","journal-title":"International Journal of Medical Informatics."},{"key":"9692_CR60","unstructured":"Karpathakis K, Murphy L, Mishra A, Joshi I. AI for healthcare: creating an international approach together. Global Digital Health Partnership; 2020. Accessed January 14, 2024. https:\/\/www.healthit.gov\/sites\/default\/files\/page\/2021-01\/GDHP_Creating%20an%20international%20approach%20together.pdf"},{"issue":"1","key":"9692_CR61","doi-asserted-by":"publisher","DOI":"10.2196\/51168","volume":"3","author":"K Karpathakis","year":"2024","unstructured":"Karpathakis, K., Pencheon, E., & Cushnan, D. (2024). Learning From international comparators of national medical imaging initiatives for AI development: Multiphase qualitative study. JMIR AI., 3(1), e51168. https:\/\/doi.org\/10.2196\/51168","journal-title":"JMIR AI."},{"issue":"11","key":"9692_CR62","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1136\/medethics-2020-107095","volume":"48","author":"CX Kerasidou","year":"2022","unstructured":"Kerasidou, C. X., Kerasidou, A., Buscher, M., & Wilkinson, S. (2022). Before and beyond trust: Reliance in medical AI. Journal of Medical Ethics, 48(11), 852\u2013856. https:\/\/doi.org\/10.1136\/medethics-2020-107095","journal-title":"Journal of Medical Ethics"},{"issue":"2","key":"9692_CR63","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s44174-023-00063-2","volume":"1","author":"B Khan","year":"2023","unstructured":"Khan, B., Fatima, H., Qureshi, A., et al. (2023). Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomedical Materials & Devices., 1(2), 731\u2013738. https:\/\/doi.org\/10.1007\/s44174-023-00063-2","journal-title":"Biomedical Materials & Devices."},{"issue":"4","key":"9692_CR64","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/15265161.2018.1431322","volume":"18","author":"SA Kraft","year":"2018","unstructured":"Kraft, S. A., Cho, M. K., Gillespie, K., et al. (2018). Beyond consent: Building trusting relationships with diverse populations in precision medicine research. The American Journal of Bioethics., 18(4), 3\u201320. https:\/\/doi.org\/10.1080\/15265161.2018.1431322","journal-title":"The American Journal of Bioethics."},{"issue":"16","key":"9692_CR65","doi-asserted-by":"publisher","DOI":"10.3390\/s22166145","volume":"22","author":"M La Salvia","year":"2022","unstructured":"La Salvia, M., Torti, E., Leon, R., et al. (2022). Deep convolutional generative adversarial networks to enhance artificial intelligence in healthcare: A skin cancer application. Sensors., 22(16), e6145. https:\/\/doi.org\/10.3390\/s22166145","journal-title":"Sensors."},{"issue":"2","key":"9692_CR66","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574\u2013582. https:\/\/doi.org\/10.1148\/radiol.2017162326","journal-title":"Radiology"},{"issue":"3","key":"9692_CR67","doi-asserted-by":"publisher","DOI":"10.3390\/informatics7030025","volume":"7","author":"TC Lee","year":"2020","unstructured":"Lee, T. C., Shah, N. U., Haack, A., & Baxter, S. L. (2020). Clinical implementation of predictive models embedded within electronic health record systems: A systematic review. Informatics., 7(3), e25. https:\/\/doi.org\/10.3390\/informatics7030025","journal-title":"Informatics."},{"key":"9692_CR68","unstructured":"Leslie D. Understanding artificial intelligence ethics and safety. arXiv preprint arXiv:190605684. Published online 2019."},{"key":"9692_CR69","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n304","author":"D Leslie","year":"2021","unstructured":"Leslie, D., Mazumder, A., Peppin, A., Wolters, M. K., & Hagerty, A. (2021). Does \u201cAI\u201d stand for augmenting inequality in the era of covid-19 healthcare? BMJ. https:\/\/doi.org\/10.1136\/bmj.n304","journal-title":"BMJ"},{"issue":"1","key":"9692_CR70","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00318-y","volume":"3","author":"RC Li","year":"2020","unstructured":"Li, R. C., Asch, S. M., & Shah, N. H. (2020). Developing a delivery science for artificial intelligence in healthcare. NPJ Digital Medicine., 3(1), e107. https:\/\/doi.org\/10.1038\/s41746-020-00318-y","journal-title":"NPJ Digital Medicine."},{"key":"9692_CR71","unstructured":"Lin B. Generative AI makes headway in healthcare. Wall Street Journal. https:\/\/www.wsj.com\/articles\/generative-ai-makes-headway-in-healthcare-cb5d4ee2. March 21, 2023. Accessed January 14, 2024."},{"issue":"8","key":"9692_CR72","doi-asserted-by":"publisher","first-page":"1626","DOI":"10.1007\/s11606-019-05035-1","volume":"34","author":"SY Lin","year":"2019","unstructured":"Lin, S. Y., Mahoney, M. R., & Sinsky, C. A. (2019). Ten ways artificial intelligence will transform primary care. Journal of General Internal Medicine, 34(8), 1626\u20131630. https:\/\/doi.org\/10.1007\/s11606-019-05035-1","journal-title":"Journal of General Internal Medicine"},{"key":"9692_CR73","unstructured":"Lind S. Health secretary announces \u00a330m tech fund and new medical school places. Pulse Today. https:\/\/www.pulsetoday.co.uk\/news\/technology\/health-secretary-announces-30m-tech-fund-and-new-medical-school-places\/. October 3, 2023. Accessed January 15, 2024."},{"key":"9692_CR74","doi-asserted-by":"publisher","first-page":"e000797","DOI":"10.1136\/leader-2023-000797","volume":"2023","author":"E Loh","year":"2023","unstructured":"Loh, E. (2023). ChatGPT and generative AI chatbots: Challenges and opportunities for science, medicine and medical leaders. BMJ Lead., 2023, e000797. https:\/\/doi.org\/10.1136\/leader-2023-000797","journal-title":"BMJ Lead."},{"issue":"5","key":"9692_CR75","doi-asserted-by":"publisher","first-page":"e139","DOI":"10.1016\/j.ijmedinf.2012.11.014","volume":"82","author":"F Magrabi","year":"2013","unstructured":"Magrabi, F., Aarts, J., Nohr, C., et al. (2013). A comparative review of patient safety initiatives for national health information technology. International Journal of Medical Informatics., 82(5), e139\u2013e148. https:\/\/doi.org\/10.1016\/j.ijmedinf.2012.11.014","journal-title":"International Journal of Medical Informatics."},{"issue":"2","key":"9692_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijmvsc.2015.6201","volume":"6","author":"T Mataracioglu","year":"2015","unstructured":"Mataracioglu, T. (2015). On the technical description of value measuring methodology. IJMVSC., 6(2), 1\u201312. https:\/\/doi.org\/10.5121\/ijmvsc.2015.6201","journal-title":"IJMVSC."},{"issue":"7788","key":"9692_CR77","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"SM McKinney","year":"2020","unstructured":"McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89\u201394. https:\/\/doi.org\/10.1038\/s41586-019-1799-6","journal-title":"Nature"},{"key":"9692_CR78","unstructured":"Ministerie van Economische Zaken en Klimaat. Strategic Action Plan for Artificial Intelligence The Netherlands.; 2019. Accessed January 15, 2024. https:\/\/wp.oecd.ai\/app\/uploads\/2021\/12\/Netherlands_Strategic_Action_Plan_for_Artificial_Intelligence.pdf"},{"issue":"6","key":"9692_CR79","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics., 19(6), 1236\u20131246. https:\/\/doi.org\/10.1093\/bib\/bbx044","journal-title":"Briefings in Bioinformatics."},{"issue":"2","key":"9692_CR80","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s11023-021-09557-8","volume":"31","author":"J M\u00f6kander","year":"2021","unstructured":"M\u00f6kander, J., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI. Minds and Machines., 31(2), 323\u2013327.","journal-title":"Minds and Machines."},{"issue":"7956","key":"9692_CR81","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor, M., Banerjee, O., Abad, Z. S. H., et al. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259\u2013265. https:\/\/doi.org\/10.1038\/s41586-023-05881-4","journal-title":"Nature"},{"issue":"1","key":"9692_CR82","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-020-10030-x","volume":"21","author":"JD Morgenstern","year":"2021","unstructured":"Morgenstern, J. D., Rosella, L. C., Daley, M. J., Goel, V., Sch\u00fcnemann, H. J., & Piggott, T. (2021). \u201cAI\u2019s gonna have an impact on everything in society, so it has to have an impact on public health\u201d: A fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health, 21(1), e40. https:\/\/doi.org\/10.1186\/s12889-020-10030-x","journal-title":"BMC Public Health"},{"key":"9692_CR83","doi-asserted-by":"crossref","unstructured":"Morley J, Morton C, Karpathakis K, Taddeo M, Floridi L. Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis. Published online April 14, 2021. Accessed May 5, 2023. http:\/\/arxiv.org\/abs\/2104.06910","DOI":"10.2139\/ssrn.3826358"},{"key":"9692_CR84","unstructured":"Morley J. On designing an algorithmically enhanced NHS: Towards a conceptual model for the successful implementation of algorithmic clinical decision support software in the national health service. PhD Thesis. University of Oxford; 2023."},{"issue":"1","key":"9692_CR85","doi-asserted-by":"publisher","DOI":"10.2196\/31623","volume":"6","author":"J Morley","year":"2022","unstructured":"Morley, J., Murphy, L., Mishra, A., Joshi, I., & Karpathakis, K. (2022). Governing data and artificial intelligence for health care: Developing an international understanding. JMIR Form Res., 6(1), e31623. https:\/\/doi.org\/10.2196\/31623","journal-title":"JMIR Form Res."},{"key":"9692_CR86","unstructured":"NHS waiting lists hit record high in England. BBC News. https:\/\/www.bbc.com\/news\/health-66188529. July 13, 2023. Accessed January 16, 2024."},{"key":"9692_CR87","unstructured":"NICE. Evidence Standards Framework for Digital Health Technologies. NICE; 2022. Accessed September 29, 2022. https:\/\/www.nice.org.uk\/corporate\/ecd7"},{"key":"9692_CR2","unstructured":"NITI Aayog. National Strategy for Artificial Intelligence #AIFORALL. Government of India; 2018. Accessed January 15, 2024. https:\/\/indiaai.gov.in\/documents\/pdf\/NationalStrategy-for-AI-Discussion-Paper.pdf"},{"key":"9692_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104679","volume":"159","author":"EM Nwanosike","year":"2022","unstructured":"Nwanosike, E. M., Conway, B. R., Merchant, H. A., & Hasan, S. S. (2022). Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics, 159, 104679. https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104679","journal-title":"International Journal of Medical Informatics"},{"key":"9692_CR89","unstructured":"Office of Chief Information Officer (OCIO). HHS Artificial Intelligence (AI) Strategy. U.S. Department of Health & Human Services. January 10, 2022. Accessed January 15, 2024. https:\/\/www.hhs.gov\/about\/agencies\/asa\/ocio\/ai\/strategy\/index.html"},{"key":"9692_CR90","unstructured":"Ordish J. Large language models and software as a medical device. MedRegs. March 3, 2023. Accessed January 15, 2024. https:\/\/medregs.blog.gov.uk\/2023\/03\/03\/large-language-models-and-software-as-a-medical-device\/"},{"key":"9692_CR91","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.954444","volume":"13","author":"M Pagliari","year":"2022","unstructured":"Pagliari, M., Chambon, V., & Berberian, B. (2022). What is new with artificial intelligence? Human\u2013agent interactions through the lens of social agency. Frontiers in Psychology, 13, 954444. https:\/\/doi.org\/10.3389\/fpsyg.2022.954444","journal-title":"Frontiers in Psychology"},{"issue":"42","key":"9692_CR92","doi-asserted-by":"publisher","DOI":"10.3346\/jkms.2020.35.e379","volume":"35","author":"CW Park","year":"2020","unstructured":"Park, C. W., Seo, S. W., Kang, N., et al. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean Medical Science, 35(42), e379. https:\/\/doi.org\/10.3346\/jkms.2020.35.e379","journal-title":"Journal of Korean Medical Science"},{"issue":"3","key":"9692_CR93","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1148\/radiol.2017171920","volume":"286","author":"SH Park","year":"2018","unstructured":"Park, S. H., & Han, K. (2018). Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 286(3), 800\u2013809. https:\/\/doi.org\/10.1148\/radiol.2017171920","journal-title":"Radiology"},{"key":"9692_CR95","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.jss.2018.03.028","volume":"228","author":"J Parreco","year":"2018","unstructured":"Parreco, J., Hidalgo, A., Parks, J. J., Kozol, R., & Rattan, R. (2018). Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. Journal of Surgical Research, 228, 179\u2013187. https:\/\/doi.org\/10.1016\/j.jss.2018.03.028","journal-title":"Journal of Surgical Research"},{"key":"9692_CR96","unstructured":"Patel N, Singhal S. What to expect in US healthcare in 2023 and beyond. McKinsey & Company. January 9, 2023. Accessed January 15, 2024. https:\/\/www.mckinsey.com\/industries\/healthcare\/our-insights\/what-to-expect-in-us-healthcare-in-2023-and-beyond#\/"},{"key":"9692_CR97","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1258\/1355819054308530","volume":"10","author":"R Pawson","year":"2005","unstructured":"Pawson, R., Greenhalgh, T., Harvey, G., & Walshe, K. (2005). Realist review\u2014A new method of systematic review designed for complex policy interventions. Journal of Health Services Research & Policy., 10, 21\u201334. https:\/\/doi.org\/10.1258\/1355819054308530","journal-title":"Journal of Health Services Research & Policy."},{"key":"9692_CR98","unstructured":"Pennic J. Canadian Government awards $49M grant to establish Canada-wide AI health data platform. HIT Consultant. May 28, 2019. Accessed January 15, 2024. https:\/\/hitconsultant.net\/2019\/05\/28\/canadian-government-awards-49m-grant-to-establish-canada-wide-ai-health-data-platform\/"},{"key":"9692_CR99","unstructured":"Pifer R. \u2018Hurtling into the future\u2019: The potential and thorny ethics of generative AI in healthcare. Healthcare Dive. https:\/\/www.healthcaredive.com\/news\/generative-AI-healthcare-gpt-potential\/648104\/. April 21, 2023. Accessed January 15, 2024."},{"issue":"9","key":"9692_CR100","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.33946","volume":"5","author":"D Plana","year":"2022","unstructured":"Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022). Randomized clinical trials of machine learning interventions in health care: A systematic review. JAMA Network Open, 5(9), e2233946. https:\/\/doi.org\/10.1001\/jamanetworkopen.2022.33946","journal-title":"JAMA Network Open"},{"issue":"2","key":"9692_CR101","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1148\/radiol.16142770","volume":"279","author":"E Pons","year":"2016","unstructured":"Pons, E., Braun, L. M. M., Hunink, M. G. M., & Kors, J. A. (2016). Natural language processing in radiology: A systematic review. Radiology, 279(2), 329\u2013343. https:\/\/doi.org\/10.1148\/radiol.16142770","journal-title":"Radiology"},{"key":"9692_CR102","unstructured":"Precedence Research. Artificial Intelligence (AI) market\u2014global industry analysis, size, share, growth, trends, regional outlook, and forecast 2023\u20132032. Precedence Research; 2023. Accessed January 15, 2024. https:\/\/www.precedenceresearch.com\/artificial-intelligence-market"},{"key":"9692_CR103","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1017\/9781108147972.027","volume-title":"Big data, health law, and bioethics","author":"N Price","year":"2018","unstructured":"Price, N. (2018). Medical malpractice and black-box medicine. In G. Cohen, H. Lynch, E. Vayena, & U. Gasser (Eds.), Big data, health law, and bioethics (pp. 295\u2013306). Cambridge University Press."},{"issue":"1","key":"9692_CR104","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1177\/0141076818815510","volume":"112","author":"S Reddy","year":"2019","unstructured":"Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22\u201328. https:\/\/doi.org\/10.1177\/0141076818815510","journal-title":"Journal of the Royal Society of Medicine"},{"key":"9692_CR106","doi-asserted-by":"crossref","unstructured":"Sahni N, Stein G, Zemmel R, Cutler DM. The potential impact of artificial intelligence on healthcare spending. National Bureau of Economic Research working paper 30857; 2023. Accessed January 15, 2024. https:\/\/www.nber.org\/system\/files\/working_papers\/w30857\/w30857.pdf","DOI":"10.3386\/w30857"},{"issue":"12","key":"9692_CR107","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002721","volume":"15","author":"S Saria","year":"2018","unstructured":"Saria, S., Butte, A., & Sheikh, A. (2018). Better medicine through machine learning: What\u2019s real, and what\u2019s artificial? PLoS Medicine, 15(12), e1002721. https:\/\/doi.org\/10.1371\/journal.pmed.1002721","journal-title":"PLoS Medicine"},{"key":"9692_CR108","unstructured":"Saudi Data & AI Authority (SDAIA). Realizing Our Best Tomorrow. Strategy Narrative. Published online October 2020. Accessed January 15, 2024. https:\/\/ai.sa\/Brochure_NSDAI_Summit%20version_EN.pdf"},{"issue":"10236","key":"9692_CR109","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/S0140-6736(20)30226-9","volume":"395","author":"N Schwalbe","year":"2020","unstructured":"Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. Lancet, 395(10236), 1579\u20131586. https:\/\/doi.org\/10.1016\/S0140-6736(20)30226-9","journal-title":"Lancet"},{"key":"9692_CR110","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8830200","volume":"2020","author":"M Senbekov","year":"2020","unstructured":"Senbekov, M., Saliev, T., Bukeyeva, Z., et al. (2020). The recent progress and applications of digital technologies in healthcare: A review. International Journal of Telemedicine and Applications, 2020, e8830200. https:\/\/doi.org\/10.1155\/2020\/8830200","journal-title":"International Journal of Telemedicine and Applications"},{"issue":"2","key":"9692_CR111","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1136\/bmjinnov-2019-000359","volume":"6","author":"MG Seneviratne","year":"2020","unstructured":"Seneviratne, M. G., Shah, N. H., & Chu, L. (2020). Bridging the implementation gap of machine learning in healthcare. BMJ Innovation, 6(2), 45\u201347. https:\/\/doi.org\/10.1136\/bmjinnov-2019-000359","journal-title":"BMJ Innovation"},{"issue":"7","key":"9692_CR112","doi-asserted-by":"publisher","DOI":"10.2196\/13659","volume":"21","author":"J Shaw","year":"2019","unstructured":"Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial Intelligence and the Implementation Challenge. Journal of Medical Internet Research, 21(7), e13659. https:\/\/doi.org\/10.2196\/13659","journal-title":"Journal of Medical Internet Research"},{"issue":"7","key":"9692_CR113","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7482","volume":"19","author":"J Shaw","year":"2017","unstructured":"Shaw, J., Shaw, S., Wherton, J., Hughes, G., & Greenhalgh, T. (2017). Studying scale-up and spread as social practice: Theoretical introduction and empirical case study. Journal of Medical Internet Research, 19(7), e244. https:\/\/doi.org\/10.2196\/jmir.7482","journal-title":"Journal of Medical Internet Research"},{"key":"9692_CR114","unstructured":"Smart Nation & Digital Government Office. National AI Strategy. Smart Nation Singapore. 2024. Accessed January 15, 2024. https:\/\/www.smartnation.gov.sg\/nais\/"},{"key":"9692_CR115","volume-title":"Studies in health technology and informatics","author":"AP Sunjaya","year":"2022","unstructured":"Sunjaya, A. P., Martin, A., & Jenkins, C. (2022). A design thinking approach to developing a clinical decision support system for breathlessness in primary care. In P. Otero, P. Scott, S. Z. Martin, & E. Huesing (Eds.), Studies in health technology and informatics. IOS Press."},{"key":"9692_CR116","unstructured":"The Government of the Republic of Korea. Toward AI World Leader Beyond IT: National Strategy for Artificial Intelligence. Artificial Intelligence Policy Division, Ministry of Science & ICT; 2019. Accessed January 14, 2024. https:\/\/wp.oecd.ai\/app\/uploads\/2021\/12\/Korea_National_Strategy_for_Artificial_Intelligence_2019.pdf"},{"issue":"1","key":"9692_CR117","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0097-x","volume":"2","author":"DSW Ting","year":"2019","unstructured":"Ting, D. S. W., Cheung, C. Y., Nguyen, Q., et al. (2019). Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: A multi-ethnic study. NPJ Digital Medicine, 2(1), e24. https:\/\/doi.org\/10.1038\/s41746-019-0097-x","journal-title":"NPJ Digital Medicine"},{"issue":"1","key":"9692_CR118","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nature Medicine"},{"key":"9692_CR36","unstructured":"Transformation Directorate. The artificial intelligence in health and care award. NHS England Transformation Directorate. Accessed January 15, 2024. https:\/\/transform.england.nhs.uk\/ai-lab\/ai-lab-programmes\/ai-health-and-care-award\/"},{"key":"9692_CR119","unstructured":"U.S. Food & Drug Administration. Marketing submission recommendations for a predetermined change control plan for artificial intelligence\/machine learning (AI\/ML)-enabled device software functions. April 3, 2023. Accessed January 14, 2024. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/marketing-submission-recommendations-predetermined-change-control-plan-artificial"},{"key":"9692_CR120","unstructured":"U.S. Food & Drug Administration. Artificial intelligence and machine learning (AI\/ML)-enabled medical devices. FDA U.S. Food & Drug Administration. October 19, 2023. Accessed January 15, 2024. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices"},{"issue":"3","key":"9692_CR121","doi-asserted-by":"publisher","first-page":"e211276","DOI":"10.1001\/jamanetworkopen.2021.1276","volume":"4","author":"B Vasey","year":"2021","unstructured":"Vasey, B., Ursprung, S., Beddoe, B., et al. (2021). Association of clinician diagnostic performance with machine learning\u2013based decision support systems: A systematic review. JAMA Network Open, 4(3), e211276\u2013e211276. https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.1276","journal-title":"JAMA Network Open"},{"issue":"6","key":"9692_CR122","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1038\/s41587-023-01789-6","volume":"41","author":"JP Vert","year":"2023","unstructured":"Vert, J. P. (2023). How will generative AI disrupt data science in drug discovery? Nature Biotechnology, 41(6), 750\u2013751. https:\/\/doi.org\/10.1038\/s41587-023-01789-6","journal-title":"Nature Biotechnology"},{"issue":"3","key":"9692_CR123","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1177\/2167702617691560","volume":"5","author":"CG Walsh","year":"2017","unstructured":"Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science: A Journal of the Association for Psychological Science, 5(3), 457\u2013469. https:\/\/doi.org\/10.1177\/2167702617691560","journal-title":"Clinical Psychological Science: A Journal of the Association for Psychological Science"},{"issue":"4","key":"9692_CR124","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174944","volume":"12","author":"SF Weng","year":"2017","unstructured":"Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE, 12(4), e0174944. https:\/\/doi.org\/10.1371\/journal.pone.0174944","journal-title":"PLoS ONE"},{"issue":"6","key":"9692_CR125","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1373\/jalm.2018.028019","volume":"3","author":"NS Whitehead","year":"2019","unstructured":"Whitehead, N. S., Williams, L., Meleth, S., et al. (2019). The effect of laboratory test-based clinical decision support tools on medication errors and adverse drug events: A laboratory medicine best practices systematic review. The Journal of Applied Laboratory Medicine, 3(6), 1035\u20131048. https:\/\/doi.org\/10.1373\/jalm.2018.028019","journal-title":"The Journal of Applied Laboratory Medicine"},{"key":"9692_CR126","doi-asserted-by":"publisher","unstructured":"Will ChatGPT transform healthcare? Nature Medicine. 2023;29(3):505\u2013506. https:\/\/doi.org\/10.1038\/s41591-023-02289-5","DOI":"10.1038\/s41591-023-02289-5"},{"issue":"1","key":"9692_CR127","doi-asserted-by":"publisher","DOI":"10.14324\/LRE.19.1.32","volume":"19","author":"S Wollscheid","year":"2021","unstructured":"Wollscheid, S., & Tripney, J. (2021). Rapid reviews as an emerging approach to evidence synthesis in education. London Review of Education., 19(1), e32. https:\/\/doi.org\/10.14324\/LRE.19.1.32","journal-title":"London Review of Education."},{"key":"9692_CR128","unstructured":"World Health Organization. Health technology assessment. who.int. 2024. Accessed January 15, 2024. https:\/\/www.who.int\/health-topics\/health-technology-assessment"},{"key":"9692_CR129","unstructured":"Yu E. Singapore launches national AI schemes, adds $133M investment to research. ZDNET. November 8, 2021. Accessed January 15, 2024. https:\/\/www.zdnet.com\/article\/singapore-launches-national-ai-schemes-adds-133m-investment-to-research\/"},{"issue":"9","key":"9692_CR130","doi-asserted-by":"publisher","DOI":"10.3390\/fi15090286","volume":"15","author":"P Zhang","year":"2023","unstructured":"Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in medicine and healthcare: Promises, opportunities and challenges. Future Internet., 15(9), e286. https:\/\/doi.org\/10.3390\/fi15090286","journal-title":"Future Internet."}],"container-title":["Minds and Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-024-09692-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11023-024-09692-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-024-09692-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T07:12:38Z","timestamp":1731654758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11023-024-09692-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":129,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["9692"],"URL":"https:\/\/doi.org\/10.1007\/s11023-024-09692-y","relation":{},"ISSN":["1572-8641"],"issn-type":[{"value":"1572-8641","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"24 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None declared.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"38"}}