The landscape of AI implementation in US hospitals

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works

Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas

Kind, A. J. H., & Buckingham, W. R. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1802313 · Cited by 3,310 · see citing works

Big Data and Machine Learning in Health Care

Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506 · see citing works

Artificial intelligence in healthcare: transforming the practice of medicine

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0095 · Cited by 1,856 · see citing works

Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review

Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. P. A. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw042 · Cited by 936 · see citing works

Clinical applications of machine learning algorithms: beyond the black box

Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E., McInnes, I. B., Barnes, M. R., & Floridi, L. (2019) · BMJ · doi:10.1136/bmj.l886 · Cited by 450 · see citing works

Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis

Estrela, M., Semedo, G., Roque, F., Ferreira, P. L., & Herdeiro, M. T. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105124 · Cited by 414 · see citing works

Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations

Badr, J., Motulsky, A., & Denis, J. (2024) · Health Policy · doi:10.1016/j.healthpol.2024.105122 · Cited by 295 · see citing works

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation

Marwaha, J. S., Landman, A. B., Brat, G. A., Dunn, T., & Gordon, W. J. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00557-1 · Cited by 241 · see citing works

Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges

Poon, E. G., Lemak, C. H., Rojas, J. C., Guptill, J., & Classen, D. C. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf065 · Cited by 180 · see citing works

Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals: Article examines uses and evaluation of artificial intelligence and predictive models in US hospitals

Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J., & Everson, J. (2025) · Health Affairs · doi:10.1377/hlthaff.2024.00842 · Cited by 104 · see citing works

Artificial Intelligence In Health And Health Care: Priorities For Action

Matheny, M. E., Goldsack, J. C., Saria, S., Shah, N. H., Gerhart, J. L., Cohen, I. G., Price, W. N., Patel, B., Payne, P., Embí, P. J., et al. (2025) · Health Affairs · doi:10.1377/hlthaff.2024.01003 · Cited by 69 · see citing works

Digital Maturity as a Predictor of Quality and Safety Outcomes in US Hospitals: Cross-Sectional Observational Study

Snowdon, A. W., Hussein, A. A., Danforth, M., Wright, A., & Oakes, R. (2024) · Journal of Medical Internet Research · doi:10.2196/56316 · Cited by 43 · see citing works

Integration of AI in healthcare requires an interoperable digital data ecosystem

Mandl, K. D., Gottlieb, D., & Mandel, J. C. (2024) · Nature Medicine · doi:10.1038/s41591-023-02783-w · Cited by 43 · see citing works

Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis

Boakye, N. F., O’Toole, C. C., Jalali, A., & Hannigan, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105887 · Cited by 41 · see citing works

Lower electronic health record adoption and interoperability in rural versus urban physician participants: a cross-sectional analysis from the CMS quality payment program

Anzalone, A. J., Geary, C. R., Dai, R., Watanabe‐Galloway, S., McClay, J. C., & Campbell, J. R. (2025) · BMC Health Services Research · doi:10.1186/s12913-024-12168-5 · Cited by 30 · see citing works

Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis

Zaka, A., Mustafiz, C., Mutahar, D., Sinhal, S., Gorcilov, J., Muston, B. T., Evans, S., Gupta, A. K., Stretton, B., Kovoor, J. J., et al. (2025) · Open Heart · doi:10.1136/openhrt-2024-002779 · Cited by 26 · see citing works

Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation

Chen, J., & Yan, A. S. (2025) · Medical Care · doi:10.1097/mlr.0000000000002110 · Cited by 22 · see citing works

New indices to track interoperability among US hospitals

Strawley, C. E., Adler‐Milstein, J., Holmgren, A. J., & Everson, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae289 · Cited by 7 · see citing works

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of CarePreprint

Hwang, Y. M., Ng, M. Y., Sahai, M. P., Pillai, M., & Hernandez‐Boussard, T. (2025) · medRxiv · doi:10.1101/2025.06.24.25330241 · Cited by 3 · see citing works

Show us the evidence for the value of medical AI

(2026) · Nature Medicine · doi:10.1038/s41591-026-04389-4 · Cited by 20 · see citing works

What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education

Hunt, V. M., Sprehe, L. K., de Lomba, W. C., Gameiro, R. R., Woite, N. L., Wade, C. G., Chen, J. H., Rougas, S. C., Fraser, H. S. F., Sullivan, A. M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02761-9 · Cited by 10 · see citing works

Vision–language foundation model driven agentic AI systems for healthcare

Chen, L., Xu, X., Qin, Y., Li, H., & Jiang, N. (2026) · The Visual Computer · doi:10.1007/s00371-026-04437-7 · Cited by 3 · see citing works

Safety and security of large language models in healthcare

Clusmann, J., Freyer, O., Ostermann, M., Ferber, D., Laleh, N. G., Hilgers, L., Kolbinger, F. R., Schneider, C. V., Downing, A., Wekenborg, M., et al. (2026) · Nature · doi:10.1038/s41586-026-10687-1 · Cited by 4 · see citing works

Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight

Arab, R. A. E., Mustafa, M. H., Almagharbeh, W. T., Ayoub, M. Y., Alsanawi, F., Almathen, F., Almosabeh, R., & Talaq, M. A. (2026) · Healthcare · doi:10.3390/healthcare14142052 · Cited by 2 · see citing works

The recursive care law: artificial intelligence reinforcing feedback loops and health inequity

Car, J., Wong, T. Y., & Atun, R. A. (2026) · The Lancet · doi:10.1016/s0140-6736(26)00982-7 · Cited by 1 · see citing works

Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study

Liu, L. (2026) · Journal of Medical Internet Research · doi:10.2196/96762 · Cited by 2 · see citing works

Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts

Vo, A., Tao, Y., Sundrup, R. Z., Mishra, A. N., Wu, D., & Nathan, L. S. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag086 · Cited by 1 · see citing works

Nurses’ AI training acceptance and tool usage: a structural equation model of individual and hospital-level factors

Guo, L., Ding, Q., Tang, M., Wei, S., Chen, L., Cao, X., Zhang, X., & Zheng, Z. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1864635 · Cited by 1 · see citing works

A lesson from the past and the future of artificial intelligence in medical decision-making

Stevenson, D. K., Katz, M., Wong, R. J., Marić, I., Rao, A. V., & Aghaeepour, N. (2026) · Journal of Perinatology · doi:10.1038/s41372-026-02824-5 · Cited by 1 · see citing works

Ethical considerations for multimodal artificial intelligence in healthcare

Kostick, K. M., Wagner, J. K., Cabrera, L. Y., & Mandl, K. D. (2026) · AI and Ethics · doi:10.1007/s43681-026-01259-0 · Cited by 1 · see citing works

Hospital AI and Robotics Adoption, Access Inequality, and County Mortality: A National Study Across 3,143 U.S. CountiesPreprint

Johnson, A., Gefen, D., & Harrison, T. (2026) · medRxiv · doi:10.64898/2026.03.10.26347904

The Inverse Care Law in the Age of AI — Geographic Disparities in Health Care Technology Access

Hwang, Y. M., Rice, B. T., & Hernandez‐Boussard, T. (2026) · NEJM AI · doi:10.1056/aip2600103

AI Implementation in Safety Net Healthcare: Understanding Barriers and StrategiesPreprint

Thomas, C., Kim, J. Y., Hasan, A., Kpodzro, S., Cortes, J., Day, B., Jensen, S., L'Huillier, S., Oden, M. O., Segura, S. Z., et al. (2026) · medRxiv · doi:10.64898/2026.04.07.26350351

We Must Be Honest About the Clinical Value of AI : When High Performance Metrics Might Hide Inconvenient Biases

Seghier, M. L. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70393

A European framework for the assessment of digital health technologies: conceptual advances, challenges, and future directions

de Waure, C., Valentini, I., Scarsi, N., Oortwijn, W., Garrett, Z., Lipska, I., Bidino, R. D., Tsiasiotis, E., Cicchetti, A., & Sacchini, D. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1809237

Continuous assurance for AI-driven clinical decision support systems

Al‐Horani, R. A., & Tadesse, A. F. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1870819

Leveraging Artificial Intelligence to Reduce Opioid Overprescription

Borbiev, S. T., Morgan, C. D., Watt, M. J., Burris, B. J., McCain, J. P., Peacock, Z. S., & Wang, T. T. (2026) · Current Surgery Reports · doi:10.1007/s40137-026-00528-5

Responsible and innovative AI for mental health care: five priority themes

Paulus, M. P., Torous, J. B., Perlis, R. H., Sarma, K. V., Ajilore, O., Khalsa, S. S., & Rodríguez, C. I. (2026) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-026-00068-x

Integration of consumer AI into healthcare pathways

Wu, Y.-L., Liu, T. Y. A., Topol, E. J., & Keane, P. A. (2026) · Nature Health · doi:10.1038/s44360-026-00195-x

Healthcare Artificial Intelligence Capability: Translating Information Contingencies into Decision Effectiveness and Operational Impact

Bizalwan, D. S., Kumar, R., & Bose, I. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10820-5

A framework for safe, ethical, and trustworthy AI deployment and integration in hospitals: a systematic review and regulatory gap analysis with emphasis on the U.S. regulatory context

Conteh, J. Y., Kapucu, N., Ebevuhe, E. F., & Adeniji, O. W. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1955958

Clinician Use of an Artificial Intelligence–Enabled Clinical Information Platform

Patel, V. R., Finlayson, S. G., Liu-Hass, M., Ray, M., Burk‐Rafel, J., Zack, T. I., & Jena, A. B. (2026) · JAMA · doi:10.1001/jama.2026.21868 · Cited by 1 · see citing works

AI Implementation in U.S. Hospitals: Regional Disparities and System-Level ImplicationsPreprint

Hwang, Y. M., Ng, M. Y., Pillai, M., Sahai, M. P., & Hernandez‐Boussard, T. (2025) · medRxiv · doi:10.1101/2025.06.27.25330441 · Cited by 2 · see citing works

Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts

Vo, A., Tao, Y., Sundrup, R. Z., Mishra, A. N., Wu, D., & Nathan, L. S. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag086 · Cited by 1 · see citing works

Predictive AI Adoption and Operational Management in the U.S. Hospitals: National Evidence on Billing, Scheduling, Evaluation, and Governance

Tanji, E., Apu, M. M. H., Rahman, M. S., Tanni, M. M. P., & Hasnain, M. A. (2026) · Journal of Next-Gen Engineering Systems · doi:10.70937/jnes.v3i02.117

Generative AI Adoption is More Widespread than Predictive AI at U.S. Hospitals

Richwine, C., Everson, J., & Nong, P. (2026) · Health Affairs Scholar · doi:10.1093/haschl/qxag301

How are US hospitals adopting artificial intelligence? Early evidence from 2022

Baten, R. B. A. (2024) · Health Affairs Scholar · doi:10.1093/haschl/qxae123 · Cited by 24 · see citing works