Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).<\/p>\n
Article<\/a>\u00a0 Varghese, C., Harrison, E. M., O\u2019Grady, G. & Topol, E. J. Artificial intelligence in surgery. Nat. Med. 30, 1257\u20131268 (2024).<\/p>\n Article<\/a>\u00a0 Methodology Committee of the Patient-Centered Outcomes Research Institute. Methodological standards and patient-centeredness in comparative effectiveness research: the PCORI perspective. J. Am. Med. Assoc. 307, 1636\u20131640 (2012).<\/p>\n Wong, T. Y. & Bressler, N. M. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. J. Am. Med. Assoc. 316, 2366\u20132367 (2016).<\/p>\n Article<\/a>\u00a0 Zeng, D., Qin, Y., Sheng, B. & Wong, T. Y. DeepSeek\u2019s \u2018low-cost\u2019 adoption across China\u2019s hospital systems: too fast, too soon?. J. Am. Med. Assoc. 333, 1866\u20131869 (2025).<\/p>\n Article<\/a>\u00a0 Shao, M. M. et al. Integrating trust into artificial intelligence for medicine: using diabetes as the exemplar disease. J. Transl. Med. https:\/\/doi.org\/10.1186\/s12967-026-07774-2<\/a> (2026).<\/p>\n Article<\/a>\u00a0 Reis, M., Reis, F. & Kunde, W. Influence of believed AI involvement on the perception of digital medical advice. Nat. Med. 30, 3098\u20133100 (2024).<\/p>\n Article<\/a>\u00a0 Fanous, A., Steffner, K. & Daneshjou, R. Patient attitudes toward the AI doctor. Nat. Med. 30, 3057\u20133058 (2024).<\/p>\n Article<\/a>\u00a0 Nong, P. & Platt, J. Patients\u2019 trust in health systems to use artificial intelligence. JAMA Netw. Open 8, e2460628 (2025).<\/p>\n Article<\/a>\u00a0 Korot, E. et al. Enablers and barriers to deployment of smartphone-based home vision monitoring in clinical practice settings. JAMA Ophthalmol. 140, 153\u2013160 (2022).<\/p>\n Article<\/a>\u00a0 Wang, T., Tan, J. B., Liu, X. L. & Zhao, I. Barriers and enablers to implementing clinical practice guidelines in primary care: an overview of systematic reviews. BMJ Open 13, e062158 (2023).<\/p>\n Article<\/a>\u00a0 Framework on Integrated, People-Centred Health Services (World Health Organization, 2016).<\/p>\n Young, A. T., Amara, D., Bhattacharya, A. & Wei, M. L. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit. Health 3, e599\u2013e611 (2021).<\/p>\n Article<\/a>\u00a0 Catapan, S. C. et al. A systematic review of consumers\u2019 and healthcare professionals\u2019 trust in digital healthcare. npj Digit. Med. 8, 115 (2025).<\/p>\n Article<\/a>\u00a0 Wickens, C. D., Lee, J. D., Liu, Y. & Gordon, S. E. An Introduction to Human Factors Engineering 2nd edn (Pearson Education Limited, 2014).<\/p>\n Lekadir, K. et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025).<\/p>\n Article<\/a>\u00a0 Solebo, A. L. & Braithwaite, T. Implications of the artificial intelligence extensions to the guidelines for consolidated standards of reporting trials and for standard protocol item recommendations for interventional trials (the CONSORT-AI and SPIRIT-AI extensions). eClinicalMedicine 26, 100536 (2020).<\/p>\n Article<\/a>\u00a0 Patient safety needs innovation. Nat. Med. 28, 1725\u20131725 (2022).<\/p>\n Nundy, S., Montgomery, T. & Wachter, R. M. Promoting trust between patients and physicians in the era of artificial intelligence. J. Am. Med. Assoc. 322, 497\u2013498 (2019).<\/p>\n Article<\/a>\u00a0 Abr\u00e0moff, M. D. et al. Considerations for addressing bias in artificial intelligence for health equity. npj Digit. Med. 6, 170 (2023).<\/p>\n Article<\/a>\u00a0 Ethics and Governance of Artificial Intelligence for Health: Large Multi-modal Models. WHO Guidance (World Health Organization, 2024).<\/p>\n Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).<\/p>\n Article<\/a>\u00a0 Palmieri, S., Robertson, C. T. & Cohen, I. G. New guidance on responsible use of AI. JAMA 335, 207\u2013208 (2026).<\/p>\n Article<\/a>\u00a0 Mello, M. M., Char, D. & Xu, S. H. Ethical obligations to inform patients about use of AI tools. J. Am. Med. Assoc. 334, 767\u2013770 (2025).<\/p>\n Article<\/a>\u00a0 Weightman, A., Clayton, P. & Coghlan, S. Informing patients about use of AI tools. J. Am. Med. Assoc. 335, 90\u201391 (2026).<\/p>\n Article<\/a>\u00a0 Angus, D. C. et al. AI, health, and health care today and tomorrow: the JAMA Summit Report on Artificial Intelligence. J. Am. Med. Assoc. 334, 1650\u20131664 (2025).<\/p>\n Article<\/a>\u00a0 Berbis, M. A. et al. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. eBioMedicine 88, 104427 (2023).<\/p>\n Article<\/a>\u00a0 Campos, H. & Salmi, L. Critical AI health literacy as liberation technology: a new skill for patient empowerment. National Academy of Medicine https:\/\/nam.edu\/wp-content\/uploads\/2025\/12\/Critical-AI-Health-Literacy_12.3.25.pdf<\/a> (2025).<\/p>\n Patient AI Rights Initiative. The Light Collective https:\/\/lightcollective.org\/patient-ai-rights\/<\/a> (2026).<\/p>\n Navathe, A. S., Clancy, C. & Glied, S. Advancing research data infrastructure for patient-centered outcomes research. J. Am. Med. Assoc. 306, 1254\u20131255 (2011).<\/p>\n Article<\/a>\u00a0 Reach Effectiveness Adoption Implementation Maintenance. RE-AIM https:\/\/re-aim.org\/<\/a> (2026).<\/p>\n Consolidated Framework for Implementation Research. CFIR https:\/\/cfirguide.org\/<\/a> (2026).<\/p>\n Salmi, L. et al. A proof-of-concept study for patient use of open notes with large language models. JAMIA Open https:\/\/doi.org\/10.1093\/jamiaopen\/ooaf021<\/a> (2025).<\/p>\n Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).<\/p>\n Article<\/a>\u00a0 Martindale, A. P. L. et al. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat. Commun. 15, 1619 (2024).<\/p>\n Article<\/a>\u00a0 Rivera, S. C. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 370, m3210 (2020).<\/p>\n Article<\/a>\u00a0 The Decide-AI Steering Group.ECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat. Med. 27, 186\u2013187 (2021).<\/p>\n Article<\/a>\u00a0 Zeng, D. et al. PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI era. Nat. Med. https:\/\/doi.org\/10.1038\/s41591-025-04178-5<\/a> (2026).<\/p>\n Article<\/a>\u00a0 Calvert, M. et al. Reporting of patient-reported outcomes in randomized trials: the CONSORT PRO extension. J. Am. Med. Assoc. 309, 814\u2013822 (2013).<\/p>\n Article<\/a>\u00a0 Calvert, M. et al. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. J. Am. Med. Assoc. 319, 483\u2013494 (2018).<\/p>\n Article<\/a>\u00a0 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).<\/p>\n Article<\/a>\u00a0 McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. A proposal for the Dartmouth summer research project on artificial intelligence: August 31, 1955. AI Mag. 27, 12\u201314 (2006).<\/p>\n Ning, Y. et al. An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI. Nat. Med. 30, 3038\u20133039 (2024).<\/p>\n Article<\/a>\u00a0 Han, R. et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit. Health 6, e367\u2013e373 (2024).<\/p>\n Article<\/a>\u00a0 Classification of Digital Health Interventions v1.0 (World Health Organization, 2018).<\/p>\n Hassan, N. et al. Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making. npj Digit. Med. 7, 332 (2024).<\/p>\n Article<\/a>\u00a0 Alderman, J. E. et al. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit. Health 7, e64\u2013e88 (2025).<\/p>\n Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n","protected":false},"excerpt":{"rendered":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019). Article\u00a0…\n","protected":false},"author":2,"featured_media":873288,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[49,48,3673,84,68741,82416,194516,392,11731,3672,303167,303168],"class_list":["post-873287","post","type-post","status-publish","format-standard","has-post-thumbnail","category-healthcare","tag-ca","tag-canada","tag-general","tag-health","tag-health-economics","tag-health-promotion-and-disease-prevention","tag-health-psychology","tag-healthcare","tag-maternal-and-child-health","tag-medicine-public-health","tag-retinal-diseases","tag-vision-disorders"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/873287","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=873287"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/873287\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/873288"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=873287"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=873287"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=873287"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}