Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review
Open accessAbstract
Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.
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The feasibility of lifecycle governance is likely to vary substantially across health systems because many recommendations assume access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures.
“The feasibility of lifecycle governance is likely to vary substantially across health systems because many recommendations assume access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures [49,50,51,52].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [49]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThese assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain.
“These assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain [49,50,51,52].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [49]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredWithout a shared framework, emergency departments struggle to distinguish safe AI tools from risky ones.
“Without a shared framework, departments struggle to distinguish safe tools from risky ones,30, 31, 32”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [32]
The Introduction identifies a lack of a systematic approach to AI procurement and implementation as a barrier, and the Conclusion says healthcare professionals find implementation challenging with little guidance. The paper discusses evaluating AI tools and their risks, but it does not specifically establish that emergency departments struggle to distinguish safe tools from risky ones. (LLM, conf=0.84)
Experimental — accuracy not yet measuredInadequate assessment of a potential partner's financial sustainability risks committing substantial clinical and technical resources to a partnership that may ultimately fail.
“Inadequate assessment of a potential partner’s financial sustainability risks committing substantial clinical and technical resources to a partnership that may ultimately fail.32,45”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [32]
The Results > Themes section (page not provided) says purchasing and implementation costs should be assessed in advance to ensure an AI tool is financially sustainable. It does not address a potential partner’s financial sustainability or say that failing to assess it risks committing clinical and technical resources to a partnership that may fail. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMuch of the review literature addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment.
“Much of the review literature instead addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment [18,19,20,29,35,37,47,49].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [49]
Global guidance emphasises equity, capacity, and context-sensitive governance for AI in health.
“this concern is reinforced by evidence that clinical AI implementation-framework authorship has been concentrated in high-income settings and by global guidance emphasising equity, capacity, and context-sensitive governance for AI in health [49,52].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [49]
Clinical AI implementation-framework authorship has been concentrated in high-income settings.
“this concern is reinforced by evidence that clinical AI implementation-framework authorship has been concentrated in high-income settings and by global guidance emphasising equity, capacity, and context-sensitive governance for AI in health [49,52].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [49]
Short online case studies based on real academic-industry partnerships can help physicians develop the skills needed to evaluate potential partners.
“Short, online case studies based on real academic-industry partnerships can help physicians develop the skills needed to evaluate potential partners.24,31,32”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [32]
The Results > Themes section discusses training clinicians to use AI tools, while the Introduction discusses procuring those tools. Neither passage addresses short online case studies, academic–industry partnerships, or teaching physicians to evaluate potential partners; searches for those terms and related vendor-selection language found no supporting passage in our copy. No page numbers were provided. (LLM, conf=0.72)
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Cites
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works
Taylor, M. J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. E. (2014) · BMJ Quality & Safety · doi:10.1136/bmjqs-2013-001862 · Cited by 1,871 · see citing works
ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD
Davenport, T. H., & Ronanki, R. (2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets42512 · Cited by 1,829 · see citing works
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
Chan, K. S., & Zary, N. (2019) · JMIR Medical Education · doi:10.2196/13930 · Cited by 765 · see citing works
Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30292-2 · Cited by 665 · see citing works
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507 · see citing works
de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499 · see citing works
Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., & Zou, J. (2021) · Nature Medicine · doi:10.1038/s41591-021-01312-x · Cited by 494 · see citing works
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08215-8 · Cited by 434 · see citing works
Identifying Ethical Considerations for Machine Learning Healthcare Applications
Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819469 · Cited by 415 · see citing works
Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A., & Shah, N. H. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa088 · Cited by 410 · see citing works
Holmes, T. C., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00700-y · Cited by 356 · see citing works
Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., Rigby, M., Scott, P. J., Vehko, T., Wong, Z. S.-Y., et al. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677903 · Cited by 347 · see citing works
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., & Fortin, J.-P. (2020) · Globalization and Health · doi:10.1186/s12992-020-00584-1 · Cited by 283 · see citing works
Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258 · see citing works
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00003-6 · Cited by 240 · see citing works
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236 · see citing works
Evaluation framework to guide implementation of AI systems into healthcare settings
Reddy, S., Rogers, W., Makinen, V.-P., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., et al. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100444 · Cited by 230 · see citing works
Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology
Mollura, D. J., Culp, M. P., Pollack, E., Battino, G., Scheel, J. R., Mango, V. L., Elahi, A., Schweitzer, A., & Dako, F. (2020) · Radiology · doi:10.1148/radiol.2020201434 · Cited by 228 · see citing works
Jiang, L., Wu, Z., Xu, X., Zhan, Y., Jin, X., Wang, L., & Qiu, Y. (2021) · Journal of International Medical Research · doi:10.1177/03000605211000157 · Cited by 219 · see citing works
Gama, F., Tyskbo, D., Nygren, J., Barlow, J., Reed, J., & Svedberg, P. (2022) · Journal of Medical Internet Research · doi:10.2196/32215 · Cited by 216 · see citing works
Reps, J. M., Schuemie, M. J., Suchard, M. A., Ryan, P. B., & Rijnbeek, P. R. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy032 · Cited by 205 · see citing works
Clinical Applications of Artificial Intelligence—An Updated Overview
Busnatu, Ș., Niculescu, A.-G., Bolocan, A., Petrescu, G. E. D., Păduraru, D. N., Năstasă, I., Lupușoru, M. O. D., Geantă, M., Andronic, O., Grumezescu, A. M., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082265 · Cited by 181 · see citing works
Ebrahim, S. B. J., Pearce, N. E., Smeeth, L. L., Casas, J. P., Jaffar, S. S., & Piot, P. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001377 · Cited by 174 · see citing works
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163 · see citing works
Self-citationEvaluating artificial intelligence in medicine: phases of clinical research
Park, Y., Jackson, G. P., Foreman, M. A., Gruen, D., Hu, J., & Das, A. K. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa033 · Cited by 150 · see citing works
Machine Learning Algorithm Validation
Maleki, F., Muthukrishnan, N., Ovens, K. L., Reinhold, C., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.004 · Cited by 144 · see citing works
Societal Issues Concerning the Application of Artificial Intelligence in Medicine
Vellido, A. (2018) · Kidney Diseases · doi:10.1159/000492428 · Cited by 134 · see citing works
Crossnohere, N. L., Elsaid, M. I., Paskett, J., Bose‐Brill, S., & Bridges, J. F. P. (2022) · Journal of Medical Internet Research · doi:10.2196/36823 · Cited by 131 · see citing works
Zhou, Q., Chen, Z.-H., Cao, Y.-H., & Peng, S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00524-2 · Cited by 126 · see citing works
Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review
Lam, T. Y. T., Cheung, M. F. K., Munro, Y. L., Lim, K. M., Shung, D., & Sung, J. J. Y. (2022) · Journal of Medical Internet Research · doi:10.2196/37188 · Cited by 120 · see citing works
Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H. R., Vanoni, M., Dreher, F., Sezerman, O. U., Marc Cuggia, A., de Tayrac, M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01634-3 · Cited by 117 · see citing works
Prediction Models — Development, Evaluation, and Clinical Application
Pencina, M. J., Goldstein, B. A., & D’Agostino, R. B. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2000589 · Cited by 116 · see citing works
Coury, J., Schneider, J. L., Rivelli, J. S., Petrik, A. F., Seibel, E., D’Agostini, B., Taplin, S. H., Green, B. B., & Coronado, G. D. (2017) · BMC Health Services Research · doi:10.1186/s12913-017-2364-3 · Cited by 108 · see citing works
Naseem, M., Akhund, R., Arshad, H., & Ibrahim, M. (2020) · Journal of Primary Care & Community Health · doi:10.1177/2150132720963634 · Cited by 105 · see citing works
The inclusion of augmented intelligence in medicine: A framework for successful implementation
Bazoukis, G., Hall, J. L., Loscalzo, J., Antman, E. M., Fuster, V., & Armoundas, A. A. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100485 · Cited by 104 · see citing works
Mészáros, J., Minari, J., & Huys, I. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.927721 · Cited by 104 · see citing works
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103 · see citing works
McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D. (2020) · CMAJ Open · doi:10.9778/cmajo.20190151 · Cited by 101 · see citing works
Evaluating pointwise reliability of machine learning prediction
Nicora, G., Rios, M., Abu–Hanna, A., & Bellazzi, R. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.103996 · Cited by 98 · see citing works
Artificial Intelligence and Machine Learning Applied at the Point of Care
Angehrn, Z., Haldna, L., Zandvliet, A. S., Gil Berglund, E., Zeeuw, J., Amzal, B., Cheung, S. Y. A., Polasek, T. M., Pfister, M., Kerbusch, T., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00759 · Cited by 93 · see citing works
AI Integration in the Clinical Workflow
Blezek, D. J., Olson-Williams, L., Missert, A., & Korfiatis, P. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00525-3 · Cited by 87 · see citing works
Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities
Hendrix, N., Veenstra, D. L., Cheng, M. M., Anderson, N. C., & Verguet, S. (2021) · Value in Health · doi:10.1016/j.jval.2021.08.015 · Cited by 85 · see citing works
Global Trend in Artificial Intelligence–Based Publications in Radiology From 2000 to 2018
West, E., Mutasa, S., Zhu, Z., & Ha, R. S. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21346 · Cited by 77 · see citing works
Rogers, W. A., Draper, H., & Carter, S. M. (2021) · Bioethics · doi:10.1111/bioe.12885 · Cited by 76 · see citing works
The Missing Pieces of Artificial Intelligence in Medicine
Gilvary, C. M., Madhukar, N. S., Elkhader, J. A., & Elemento, O. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.001 · Cited by 76 · see citing works
Jungblut, L., Blüthgen, C., Polacin, M., Messerli, M., Schmidt, B. T., Euler, A., Alkadhi, H., Frauenfelder, T., & Martini, K. (2021) · Investigative Radiology · doi:10.1097/rli.0000000000000814 · Cited by 75 · see citing works
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., Mulligan, D., Patel, M., Raji, I. D., Sehgal, A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594089 · Cited by 55 · see citing works
Self-citationZhang, J., Whebell, S. F., Gallifant, J., Budhdeo, S., Mattie, H., Lertvittayakumjorn, P., Lopez, M. D. P. A., Tiangco, B. J., Gichoya, J. W., Ashrafian, H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00032-2 · Cited by 52 · see citing works
From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods
Smith, M., Sattler, A., Hong, G., & Lin, S. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06394-w · Cited by 51 · see citing works
Machine Learning for Health: Algorithm Auditing & Quality Control
Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., et al. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01783-y · Cited by 48 · see citing works
Singer, S. J., Kellogg, K. C., Galper, A. B., & Viola, D. (2021) · Health Care Management Review · doi:10.1097/hmr.0000000000000324 · Cited by 31 · see citing works
Bosmans, H., Zanca, F., & Gelaude, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.006 · Cited by 29 · see citing works
Bakker, L. J., Aarts, J. E. C. M., Groot, C. A. U., & Redekop, K. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01682-9 · Cited by 9 · see citing works
Woo, M., Alhanti, B., Lusk, S., Dunston, F., Blackwelder, S. P., Lytle, K. S., Goldstein, B. A., & Bedoya, A. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030104 · Cited by 8 · see citing works
Using the Plan, Do, Study, Act cycle to enhance a patient feedback system for older adults
McGowan, M., & Reid, B. B. (2018) · British Journal of Nursing · doi:10.12968/bjon.2018.27.16.936 · Cited by 7 · see citing works
Hamilton, J. L., Snuggerud, A., Meihls, S., Toledo, H., & Stewart, J. (2021) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2021.11.006 · Cited by 6 · see citing works
Browning, T. G., O’Neill, T., Ng, Y. S., Fielding, J. R., & Peshock, R. M. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.08.016 · Cited by 5 · see citing works
Flagged first, then by citations
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Sorted by citations
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S. G., Çöltekin, A., & Peter, M. K. (2024) · Journal of Medical Internet Research · doi:10.2196/67485 · Cited by 113 · see citing works
Examining human-AI interaction in real-world healthcare beyond the laboratory
Wekenborg, M., Gilbert, S., & Kather, J. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01559-5 · Cited by 49 · see citing works
Advancing healthcare AI governance through a comprehensive maturity model based on systematic review
Hussein, R., Zink, A., Ramadan, B., Howard, F. M., Hightower, M., Shah, S., & Beaulieu-Jones, B. K. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02418-7 · Cited by 42 · see citing works
Jenko, S., Papadopoulou, E., Kumar, V., Overman, S. S., Krepelkova, K., Wilson, J., Dunbar, E. L., Spice, C., & Exarchos, T. P. (2025) · AI · doi:10.3390/ai6060116 · Cited by 51 · see citing works
Recognising errors in AI implementation in radiology: A narrative review
Stogiannos, N., Cuocolo, R., D’Antonoli, T. A., dos Santos, D. P., Harvey, H., Huisman, M., Koçak, B., Kotter, E., Lekadir, K., Shelmerdine, S. C., et al. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112311 · Cited by 31 · see citing works
Bailo, P., Nittari, G., Pesel, G., Basello, E., Spasari, T., & Ricci, G. (2026) · Sci · doi:10.3390/sci8020036 · Cited by 22 · see citing works
Ardito, V., Cappellaro, G., Compagni, A., Petracca, F., & Preti, L. M. (2025) · Digital Health · doi:10.1177/20552076251355680 · Cited by 14 · see citing works
Bhimani, M., Miller, A., Agnew, J. D., Ausin, M. S., Raglow-Defranco, M., Mangat, H. S., Voisard, M., Taylor, M., Bierman-Lytle, S., Parikh, V., et al. (2025) · medRxiv · doi:10.1101/2025.03.17.25324157 · Cited by 13 · see citing works
Scoping review on the economic aspects of machine learning applications in healthcare
von Gerich, H., Helenius, M., Hörhammer, I., Moen, H., & Peltonen, L. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106103 · Cited by 10 · see citing works
Artificial intelligence (AI) in health systems: introducing a ‘Do Good’ approach
Papadopoulou, E., Exarchos, T. P., Jenko, S., Krepelkova, K., & Namorado, J. (2025) · AI and Ethics · doi:10.1007/s43681-025-00757-x · Cited by 8 · see citing works
Bernasconi, L., Avakyan, G., Hovaguimian, F., & Großmann, R. (2025) · Ethics & Human Research · doi:10.1002/eahr.60014 · Cited by 9 · see citing works
Evolution of an Artificial Intelligence-Powered Application for Mammography
Vasilev, Y. A., Андреевич, Р. Д., Vladzymyrskyy, A. V., Omelyanskaya, O. V., Дмитриевич, П. Л., Михайлович, Ш. И., Nikitin, E. D., Kapninskiy, A., & Arzamasov, K. M. (2025) · Diagnostics · doi:10.3390/diagnostics15070822 · Cited by 5 · see citing works
El Arab, R. A., Mustafa, M. H., Almagharbeh, W. T., Saleem, N. H., Al Abdulmohsen, S., Boathab, R., & Bu Washl, M. (2026) · Healthcare · doi:10.3390/healthcare14111459 · Cited by 4 · see citing works
“Subtheme 3.1: Incomplete coverage of lifecycle frameworks The systematic synthesis of implementation frameworks came from Khan et al. (2024) [49], who reviewed 25 clinical AI implementation frameworks and mapped them onto a modified Plan–”
InfluentialVladzymyrskyy, A. V., Vasilev, Y. A., Arzamasov, K. M., Kazarinova, V., & Васильевна, А. Е. (2025) · N N Priorov Journal of Traumatology and Orthopedics · doi:10.17816/vto633860 · Cited by 2 · see citing works
A nursing perspective on human-AI collaboration in personalized breast cancer care pathways
Zhang, J., Zhao, X., Tao, J.-H., & Su, D.-C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1784401 · Cited by 2 · see citing works
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
Cheruku, V. R. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.4.3 · Cited by 2 · see citing works
Kpodzro, S., Kim, J. Y., Hasan, A., Thomas, C., Draugelis, M. E., Jeffries, S. M., Beecy, A., Stroum, C., Valladares, A., Eigen, Z. J., et al. (2026) · NEJM AI · doi:10.1056/aip2500985 · Cited by 3 · see citing works
Self-citationRabiee, P. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_9 · Cited by 2 · see citing works
Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges
Zhao, H., Xue, T., Zhang, Y., Lu, Y., Wang, D., Yin, P., Jiang, L., Liu, D., & Wang, Y. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1740915 · Cited by 1 · see citing works
Andersen, F., Løkkeberg, S. E. T., Hardeland, C., & Leonardsen, A. L. (2026) · Journal of Primary Care & Community Health · doi:10.1177/21501319251408672 · Cited by 2 · see citing works
Aliaga, C. A. T., Fuentes, V. G. H., Karim, H. B. A., AlDahoul, N., & Tan, M. J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1894116 · Cited by 1 · see citing works
Population health management fit lifecycles in analytics
Henry, J. A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1496945 · Cited by 1 · see citing works
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S. G., Çöltekin, A., & Peter, M. K. (2024) · doi:10.2196/preprints.67485 · Cited by 1 · see citing works
Dash, D., Macalalad, J., Lum, D. L., Baker, M. W., Dalsey, W. C., Sangal, R. B., Dayton, J. R., Waseem, T. C., Sikka, N. K., & Chu, A. L. (2026) · JACEP Open · doi:10.1016/j.acepjo.2026.100503
Weber, S. G. (2025) · American Journal of Medical Quality · doi:10.1097/jmq.0000000000000271
Palozzi, G., Martino, M. D., Chirico, A., & Antonucci, G. (2026) · MANAGEMENT CONTROL · doi:10.3280/maco2025-003006
Evaluating the Safety of Medical LLMs for Patient Use
Vishva, R., Fahmitha, F. F., Indhu, S., Gayathri, R., & Saranraj, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7862-6.ch006
Machine Learning to Identify Bacteremia and Meningitis in Febrile Infants: A Systematic Review
Ferraris, G., Tazzoli, S., Folisi, C., Pisani, L., Rashidi, H. H., Kuppermann, N., & Bressan, S. (2026) · PEDIATRICS · doi:10.1542/peds.2025-074576
Ryvlin, P., Beniczky, S., Aurlien, H., Bernini, A., Jones, G. D., Kariuki, S. M., Spahr, A., Tveit, J., & Sen, A. (2026) · European Journal of Neurology · doi:10.1111/ene.70740
Sabala, A., Davins, M., Cano, M., Bonet, G., Mariscal, A., & Valero, P. (2026) · Journal of Healthcare Quality Research · doi:10.1016/j.jhqr.2026.101243
Artificial Intelligence in Sleep Medicine
Dhafar, H. O., & BaHammam, A. S. (2026) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2026.08.001
Xieyi, W., & Wang, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1985884
You, Y., Liu, Y., & Zhao, R. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103582
From principles to practice: an Implementation Decision-Tool for Ethical AI in clinical governance
Zohny, H., Singh, I., & Savulescu, J. (2026) · Journal of Medical Ethics · doi:10.1136/jme-2025-111546
Kiwanuka, F. (2026) · Digital Health · doi:10.1177/20552076261497803
- Authors
- Khan, S. D., Hoodbhoy, Z., Raja, M. H. R., Kim, J. Y., Hogg, H. D. J., Manji, A. A. A., Gulamali, F., Hasan, A., Shaikh, A., Tajuddin, S., Khan, N. S., Patel, M. R., Balu, S., Samad, Z., & Sendak, M. P.
- Affiliations
- Aga Khan University (PK)
- Duke University (US)
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Corresponding author
- Sendak, Mark P.
- ORCID
- Venue
- PLOS Digital Health
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-05-29
- Volume
- 3
- Issue
- 5
- Pages
- e0000514
- ISSN
- 2767-3170 (ISSN-L)
- PMC
- PMC11135672
- OpenAlex
- W4399118706
- Cited by
- 36
- References
- 62
- Citations by year
- 2026: 22 · 2025: 12 · 2024: 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLOS Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eCommons - AKU (Aga Khan University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Patrick J. McGovern Foundation (383000239)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Keywords
- artificial intelligence in healthcare, AI procurement, clinical implementation of AI, AI transparency, low-income countries, knowledge translation, data sharing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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