External validation of AI models in health should be replaced with recurring local validation
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Evaluating AI within health care organizations using their patients' data is a strategy to address concerns about inaccuracy and limit inaccuracies.
“One strategy to address this concern and limit inaccuracies is to evaluate AI within health care organizations using their patients’ data.4,20,21”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [20]
There are widespread concerns that the performance of AI tools in early validation studies may not reflect their use in practice.
“There are widespread concerns that the performance of these tools in early validation studies may not reflect their use in practice.19,20”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [20]
Heterogeneity in model performance should be expected based on differences in populations and measurement procedures between locations, settings, or time periods.
“Second, heterogeneity in model performance should be expected based on differences in populations and measurement procedures between locations, settings, or time periods.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [103]
Related works have instrumented quantitative (completeness, correctness, conciseness) and qualitative (content, language, structure) measurements to distinguish synthesized clinical reports from human reports.
“Therefore, related works have instrumented quantitative (completeness, correctness, conciseness) and qualitative (content, language, structure) measurements to dissect the riveting characters that distinguish synthesized clinical reports from human reports46–49.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [49]
The training material may prime the dexterity of the resultant module.
“This phenomenon reflects that the training material may prime the dexterity of the resultant module49.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [49]
Cites
External validation of prognostic models: what, why, how, when and where?
Ramspek, C. L., Jager, K. J., Dekker, F. W., Zoccali, C., & van Diepen, M. (2021) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa188 · Cited by 887 · see citing works
External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review
Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 455 · see citing works
Making Machine Learning Models Clinically Useful
Shah, N. H., Milstein, A., & Bagley, P. (2019) · JAMA · doi:10.1001/jama.2019.10306 · Cited by 294 · see citing works
Self-citationThere is no such thing as a validated prediction model
Van Calster, B., Steyerberg, E. W., Wynants, L., & van Smeden, M. (2023) · BMC Medicine · doi:10.1186/s12916-023-02779-w · Cited by 286 · see citing works
A framework for the oversight and local deployment of safe and high-quality prediction models
Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac078 · Cited by 119 · see citing works
Self-citationThe epic sepsis model falls short-the importance of external validation
Habib, A. R., Lin, A. L., & Grant, R. W. (2021) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2021.3333 · Cited by 101 · see citing works
Singh, H., Mhasawade, V., & Chunara, R. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000023 · Cited by 67 · see citing works
Granlund, T., Stirbu, V., & Mikkonen, T. (2021) · SN Computer Science · doi:10.1007/s42979-021-00726-1 · Cited by 53 · see citing works
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FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine
Warraich, H. J., Tazbaz, T., & Califf, R. M. (2025) · JAMA · doi:10.1001/jama.2024.21451 · Cited by 332 · see citing works
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Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 309 · see citing works
AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence
Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA · doi:10.1001/jama.2025.18490 · Cited by 195 · see citing works
Self-citationA Nationwide Network of Health AI Assurance Laboratories
Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B. (2024) · JAMA · doi:10.1001/jama.2023.26930 · Cited by 114 · see citing works
Self-citationVan Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
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 Spinal Imaging and Patient Care: A Review of Recent Advances
Lee, S., Jung, J., Mahatthanatrakul, A., & Kim, J.-S. L. (2024) · Neurospine · doi:10.14245/ns.2448388.194 · Cited by 76 · see citing works
Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation
Li, C.-Y., Chang, K.-J., Yang, C.-F., Wu, H.-Y., Chen, W., Bansal, H., Chen, L., Yang, Y.-P., Chen, Y.-C., Chen, S.-P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57426-0 · Cited by 75 · see citing works
“38 Hence, we suggest enrolling diverse disease etiologies with the aim of differential diagnosis to improve MLLM generalization on border brain CT features.”
Practical AI application in psychiatry: historical review and future directions
Sun, J., Lu, T., Shao, X., Han, Y., Xia, Y., Zheng, Y., Wang, Y., Li, X., Ravindran, A. V., Fan, L., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03072-3 · Cited by 78 · see citing works
Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems
Rosenthal, J. T., Beecy, A., & Sabuncu, M. R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01674-3 · Cited by 59 · see citing works
BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos
Nakayama, L. F., Restrepo, D. S., de Matos, J. C. R. G., Ribeiro, L. Z., Malerbi, F. K., Celi, L. A., & Regatieri, C. V. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000454 · Cited by 55 · see citing works
Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review
Salama, V., Godinich, B. M., Geng, Y., Humbert‐Vidan, L., Maule, L., Wahid, K. A., Naser, M. A., He, R., Mohamed, A., Fuller, C. D., et al. (2024) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2024.07.025 · Cited by 51 · see citing works
Castagno, S., Birch, M., van der Schaar, M., & McCaskie, A. W. (2024) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2024-225872 · Cited by 39 · see citing works
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Self-citationKhan, S. S., Greenland, P., Hayman, L. L., Khera, R., Návar, A. M., Pencina, M. J., Reza, N., Shah, S. H., Shanbhag, S. M., Weber, B., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001401 · Cited by 42 · see citing works
Self-citationOng, A. Y., Taribagil, P., Sevgi, M., Kale, A. U., Dow, E. R., Macdonald, T., Kras, A., Maniatopoulos, G., Liu, X., Keane, P. A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01726-8 · Cited by 39 · see citing works
Joshi, S. D., Urteaga, I., van Amsterdam, W. A. C., Hripcsak, G. M., Elias, P. A., Recht, B., Elhadad, N., Fackler, J. C., Sendak, M., Wiens, J., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae301 · Cited by 36 · see citing works
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Recognising errors in AI implementation in radiology: A narrative review
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Chiou, A. S., Omiye, J. A., Gui, H., Swetter, S. M., Ko, J., Gastman, B., Arbesman, J., Cai, Z. R., Gevaert, O., Sadée, C., et al. (2025) · NEJM AI · doi:10.1056/aidbp2400732 · Cited by 28 · see citing works
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Ranard, B. L., Park, S., Jia, Y., Zhang, Y., Alwan, F. S., Celi, L. A., & Lusczek, E. R. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154796 · Cited by 27 · see citing works
Giesa, N., Sekutowicz, M., Rubarth, K., Spies, C., Balzer, F., Haufe, S., & Boie, S. D. (2024) · Communications Medicine · doi:10.1038/s43856-024-00681-x · Cited by 25 · see citing works
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Jain, S. S., Goto, S., Hall, J. L., Khan, S. S., MacRae, C. A., Ofori, C., Pegus, C., Pencina, M., Peterson, E. D., Schwamm, L. H., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001400 · Cited by 23 · see citing works
Self-citationLiu, G. S., Fereydooni, S., Lee, M. C., Polkampally, S., Huynh, J. D., Kuchibhotla, S., Shah, M. M., Ayoub, N., Capasso, R., Chang, M. T., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01693-0 · Cited by 20 · see citing works
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Barzilai, D. H., Sudri, K., Goshen, G., Klang, E., Zimlichman, E., Barbash, I. M., & Cohen, M. S. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102152 · Cited by 20 · see citing works
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Richter, F., Holmes, E., Richter, F., Guttmann, K., Duong, S. Q., Gangadharan, S., Schadt, E., Salmasian, H., Gelb, B. D., & Glicksberg, B. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02000-7 · Cited by 24 · see citing works
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Jiménez-Gracia, L., Maspero, D., Aguilar-Fernández, S., Craighero, F., Boulougouri, M., Ruiz, M., Marchese, D., Caratù, G., Liñares-Blanco, J., Berasategi, M., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04126-3 · Cited by 20 · see citing works
Bryant, A. K., Zamora‐Resendiz, R., Dai, X., Morrow, D., Lin, Y., Jungles, K. M., Rae, J. M., Tate, A., Pearson, A. N., Jiang, R., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7253 · Cited by 18 · see citing works
Jackson, B. R., Sendak, M., Solomonides, A. E., Balu, S., & Sittig, D. F. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae296 · Cited by 16 · see citing works
Oikonomou, E. K., Batinica, B., Dhingra, L. S., Aminorroaya, A., Coppi, A. C., & Khera, R. (2026) · NEJM AI · doi:10.1056/aioa2500588 · Cited by 17 · see citing works
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Bender, A., Thomas, M. C., Scannell, J. W., Shaywitz, D. A., Ghiandoni, G. M., Greener, J. G., Pruteanu, L.-L., Jacobson, R. D., Handa, K., Hirano, M., et al. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01496-2 · Cited by 22 · see citing works
Huang, L., Hu, J., Cai, Q., Fu, G., Bai, Z., Liu, Y., Zheng, J., & Meng, Z. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgae062 · Cited by 15 · see citing works
Guerreiro, J., Garriga, R., Bagén, T. L., Sharma, B., Karnik, N. S., & Matic, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01203-8 · Cited by 15 · see citing works
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Pencina, M. J., McCall, J., & Economou-Zavlanos, N. J. (2024) · JAMA · doi:10.1001/jama.2024.14026 · Cited by 15 · see citing works
Self-citationLandolfo, C., Ceusters, J., Valentin, L., Froyman, W., Van Gorp, T., Heremans, R., Baert, T., Wouters, R., Vankerckhoven, A., Van Rompuy, A.-S., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02578-x · Cited by 14 · see citing works
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Ostmeier, S., Axelrod, B., Liu, Y., Yu, Y., Jiang, B., Yuen, N., Pulli, B., Verhaaren, B. F. J., Kaka, H., Wintermark, M., et al. (2024) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2023-021283 · Cited by 14 · see citing works
Karway, G. K., Koyner, J. L., Caskey, J., Spicer, A. B., Carey, K. A., Gilbert, E. R., Dligach, D., Mayampurath, A. M., Afshar, M., & Churpek, M. M. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad109 · Cited by 13 · see citing works
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Restrepo, D. S., Quion, J. M., Novaes, F. D. C., Costa, I. D. A., Vásquez-Venegas, C., Bautista, A. N., Quiminiano, E., Lim, P. A., Mwavu, R., Celi, L. A., et al. (2024) · Seminars in Ophthalmology · doi:10.1080/08820538.2024.2308248 · Cited by 12 · see citing works
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Moukheiber, D., Restrepo, D. S., Cajas, S. A., Arbeláez, M. P., Celi, L. A., Kuo, K.-T., López, D. M., Moukheiber, L., Moukheiber, M., Moukheiber, S., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03366-1 · Cited by 12 · see citing works
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Fernandes, T. R. S., Teles, A. S., Fernandes, J. R. N., Lima, L. D. B., Sousa, D. L., Soares, R. D. C., & Teixeira, S. S. (2025) · IEEE Access · doi:10.1109/access.2025.3584904 · Cited by 12 · see citing works
Negoi, I. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i18.106670 · Cited by 11 · see citing works
Antaki, F., Hammana, I., Tessier, M.-C., Boucher, A., Jetté, M. L. D., Beauchemin, C., Hammamji, K., Ong, A. Y., Rhéaume, M., Gauthier, D., et al. (2024) · JMIR Diabetes · doi:10.2196/59867 · Cited by 11 · see citing works
Lam, J. Y., Lu, X., Shashikumar, S. P., Lee, Y. S., Miller, M., Pour, H., Boussina, A. E., Pearce, A. K., Malhotra, A., & Nemati, S. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae141 · Cited by 10 · see citing works
CALSTER, B. V., van Smeden, M., van Amsterdam, W. A. C., Coemans, M., Wynants, L., & Steyerberg, E. W. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042324-123749 · Cited by 12 · see citing works
Rosen, K. L., & Mandl, K. D. (2025) · medRxiv · doi:10.1101/2025.08.26.25334477 · Cited by 10 · see citing works
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de Matos, J. C. R. G., Gallifant, J., Chowdhury, A., Economou-Zavlanos, N., Charpignon, M., Gichoya, J. W., Celi, L. A., Nazer, L. H., King, H., & Wong, A.-K. I. (2024) · Critical Care Clinics · doi:10.1016/j.ccc.2024.05.011 · Cited by 9 · see citing works
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Swaminathan, A., Srivastava, U., Tu, L., López, I., Shah, N. H., & Vickers, A. J. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00184-2 · Cited by 9 · see citing works
Self-citationFaviez, C., Chen, X., Garcelon, N., Zaidan, M., Billot, K., Petzold, F., Faour, H., Douillet, M., Rozet, J., Cormier‐Daire, V., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02538-8 · Cited by 10 · see citing works
Hong, M., Kang, R.-R., Yang, J. H., Rhee, S. J., Lee, H., Kim, Y.-G., Lee, K. Y., Kim, H., Lee, Y. S., Youn, T., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/65994 · Cited by 9 · see citing works
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Charpignon, M., Celi, L. A., Cobanaj, M., Eber, R., Fiske, A., Gallifant, J., Li, C., Lingamallu, G., Petushkov, A., & Pierce, R. L. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000486 · Cited by 8 · see citing works
Li, T., Xu, K., Krishnan, A. R., Gao, R., Kammer, M. N., Antic, S., Xiao, D., Knight, M. A., Martinez, Y., Paez, R., et al. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.230506 · Cited by 8 · see citing works
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Alessandro, L., Bianciotti, N., Salama, L., Volmaro, S., Navarrine, V., Ameghino, L., Arena, J., Bestoso, S., Bruno, V. A., Castillo‐Torres, S. A., et al. (2025) · Movement Disorders · doi:10.1002/mds.30168 · Cited by 8 · see citing works
Ramesh, D., Manickavel, P., Ghosh, S., & Bhat, M. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.102611 · Cited by 9 · see citing works
Liang, Y. G., Fan, L., Teixeira-Pinto, A. M., Liew, G., & White, A. J. R. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02321-7 · Cited by 8 · see citing works
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Salama, V., Godinich, B. M., Geng, Y., Humbert‐Vidan, L., Maule, L., Wahid, K. A., Naser, M. A., He, R., Mohamed, A., Fuller, C. D., et al. (2023) · medRxiv · doi:10.1101/2023.12.06.23299610 · Cited by 8 · see citing works
Nejad, M. C., Matthiesen, R. V., Dukovska‐Popovska, I., Jakobsen, T. L., & Johansen, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105631 · Cited by 7 · see citing works
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Ran, A. R., Lui, C. H., Tham, Y. C., Cheng, C.-Y., Lam, C. Y., Cheung, W. L., Chan, S. T., Ma, H. N., Chow, R. W. L. C., Yang, D., et al. (2025) · Eye · doi:10.1038/s41433-025-03878-z · Cited by 7 · see citing works
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Walsh, C. G., Long, Y., Novak, L. L., Salwei, M. E., Tillman, B. F., French, B. C., Mixon, A. S., Law, M. E., Franklin, J., & Embí, P. J. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.35137 · Cited by 7 · see citing works
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Gutierrez, K. L. T., & Viacrusis, P. M. (2023) · Journal of Medicine University of Santo Tomas · doi:10.35460/2546-1621.2023-0081 · Cited by 7 · see citing works
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Li, S., Li, X., Yu, K., Wu, Q., Miao, D., Zhu, M., Yan, M., Ke, Y., D’Agostino, D., Ning, Y., et al. (2025) · Health Data Science · doi:10.34133/hds.0321 · Cited by 7 · see citing works
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Jafrasteh, B., Adeli, E., Pohl, K. M., Kuceyeski, A. F., Sabuncu, M. R., & Zhao, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-12026-2 · Cited by 6 · see citing works
Xue, M., Yang, J., Wang, H., Yan, Z., Chen, X., Gao, W., Luo, R., Lv, X., & Ye, Z. (2025) · Journal of Anesthesia and Translational Medicine · doi:10.1016/j.jatmed.2025.06.003 · Cited by 7 · see citing works
Nakayama, L., Ribeiro, L. Z., Malerbi, F., & Regatieri, C. (2025) · International Journal of Retina and Vitreous · doi:10.1186/s40942-024-00596-8 · Cited by 11 · see citing works
Rennie, O. (2024) · Medicine Health Care and Philosophy · doi:10.1007/s11019-024-10206-x · Cited by 6 · see citing works
The FDA and the Cardiovascular Community
Warraich, H. J., & Califf, R. M. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.013 · Cited by 6 · see citing works
Gao, J., Chen, G., O’Rourke, A. P., Caskey, J., Carey, K. A., Oguss, M., Stey, A., Dligach, D., Miller, T., Mayampurath, A., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae071 · Cited by 5 · see citing works
Ong, A. Y., Kale, A. U., Antoun, J., Hogg, H. D. J., Hammond, B., Keane, P. A., Pearson, R. V., Harvey, H., & Denniston, A. K. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02407-w · Cited by 5 · see citing works
Ong, A. Y., Struyven, R., Denniston, A. K., Merle, D. A., Engelmann, J., Kim, H., Zhou, Y., Keane, P. A., & Laíns, I. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02463-2 · Cited by 5 · see citing works
From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis
Wang, F., Chen, J., Zheng, L., & Huang, X. (2026) · Journal of bone oncology · doi:10.1016/j.jbo.2026.100753 · Cited by 5 · see citing works
Khumalo, A. C., & Alaqra, A. S. (2026) · JMIR Human Factors · doi:10.2196/83240 · Cited by 6 · see citing works
Zhang, X., Liang, X., Fu, Z., Zhou, Y., Fang, Y., Liu, X., Yuan, Q., Liu, R., Hong, Q., & Liu, C. (2024) · Emergency and Critical Care Medicine · doi:10.1097/ec9.0000000000000126 · Cited by 5 · see citing works
Lost, J., Ashraf, N., Jekel, L., von Reppert, M., Tillmanns, N., Willms, K., Merkaj, S., Petersen, G. C., Avesta, A., Ramakrishnan, D. S., et al. (2024) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdae157 · Cited by 5 · see citing works
A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at ScalePreprint
Burger, M., Chopard, D., Lichtner, G., Londschien, M., Sergeev, F., Fuchs, M., Yèche, H., Kuznetsova, R., Faltys, M., Gerdes, E., et al. (2025) · medRxiv · doi:10.1101/2025.07.25.25331635 · Cited by 7 · see citing works
Nakayama, L., Ribeiro, L. Z., Tabuse, C., Malerbi, F., & Regatieri, C. (2025) · AJO International · doi:10.1016/j.ajoint.2025.100194 · Cited by 4 · see citing works
Updated critical quality criteria for real-world artificial intelligence implementation
Oikonomou, E. K., Asselbergs, F. W., & Vardas, P. E. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag121 · Cited by 4 · see citing works
Rider, N. L., Bastarache, L. A., Anderson, J. T., Behrens, E. M., Chaimowitz, N. S., Chandrakasan, S., Hartline, B. K., Liu, A. Y., Marsh, R. A., & Connelly, J. A. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.03.040 · Cited by 4 · see citing works
Gowda, V., Bizzo, B. C., & Dreyer, K. J. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.05.032 · Cited by 4 · see citing works
External Validation of a Multimodal Model for Predicting Outcomes in Preterm Newborns
Routier, L., Touati, S., Ghostine, G., Wallois, F., Querné, L., & Bourel‐Ponchel, E. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.23029 · Cited by 4 · see citing works
Implementation of machine learning in emergency departments: A systematic review
Hosseini, B., Patel, A., Landes, M., Vaillancourt, S., Mamdani, M. M., Maruthananth, K., Matharu, N., Pathan, Z., Sivapragasam, K., Ruangsomboon, O., et al. (2026) · Digital Health · doi:10.1177/20552076251411209 · Cited by 5 · see citing works
Arunga, S., Morley, K., Kwaga, T., Morley, M., Nakayama, L. F., Mwavu, R., Kaggwa, F., Ssempiira, J., Celi, L. A., Haberer, J. E., et al. (2024) · JMIR Formative Research · doi:10.2196/59914 · Cited by 4 · see citing works
Campagner, A., Milella, F., Banfi, G., & Cabitza, F. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02602-3 · Cited by 4 · see citing works
Zhang, T., Zhao, X., Yeo, B. T. T., Huo, X., Eickhoff, S. B., & Ji, C. (2026) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf218 · Cited by 4 · see citing works
Frechman, E., Jaeger, B. C., Kowalkowski, M., Williamson, J. D., Lenoir, K. M., Palakshappa, J. A., Wells, B. J., Callahan, K. E., Pajewski, N. M., & Gabbard, J. L. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf062 · Cited by 3 · see citing works
The role of AI in oncology: present applications and future horizons
Weitzner, A. S., Singla, N., & Rai, A. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01408-y · Cited by 4 · see citing works
Entenmann, C. J., Kersting, K., Vajkoczy, P., & Zdunczyk, A. (2025) · Brain and Spine · doi:10.1016/j.bas.2025.104283 · Cited by 3 · see citing works
Thiers, F. A., & Harned, Z. (2024) · doi:10.31219/osf.io/5ks37 · Cited by 4 · see citing works
Explainable machine learning to identify patients at risk of developing hospital acquired infectionsPreprint
Creagh, A. P., Pease, T., Ashworth, P., Bradley, L. J., & Duport, S. (2024) · medRxiv · doi:10.1101/2024.11.13.24317108 · Cited by 4 · see citing works
Du, H., Yu, J., Chen, D., Wu, J., Xue, E., Zhou, Y., Pan, X., Shao, J., & Ye, Z. (2025) · Journal of Medical Internet Research · doi:10.2196/72636 · Cited by 6 · see citing works
Bai, T., Jiang, K., Yu, Y., Qie, S., Wang, C., Wang, B., & Zhang, W. (2026) · Future Internet · doi:10.3390/fi18020095 · Cited by 2 · see citing works
Lipskiy, N., & Flowerday, S. (2026) · Future Internet · doi:10.3390/fi18050232 · Cited by 2 · see citing works
Adeyeye, S. (2026) · Journal of Stored Products Research · doi:10.1016/j.jspr.2026.103138 · Cited by 1 · see citing works
Governing Clinical Readiness Claims Derived from Medical AI Benchmark Results
Dong, Y., Cheng, J., Ding, C., & Lu, R. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02445-7 · Cited by 2 · see citing works
- Authors
- Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H.
- Affiliations
- University of Oxford (GB)
- Duke University (US)
- Stanford Medicine (US)
- Corresponding author
- Youssef, Alexey
- Venue
- Nature Medicine
- Published
- 2023-10-18
- Volume
- 29
- Issue
- 11
- Pages
- 2686-2687
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4387763575
- Cited by
- 170
- References
- 9
- Citations by year
- 2026: 61 · 2025: 61 · 2024: 44 · 2023: 4
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- external validation, local validation, artificial intelligence models, machine learning, medical device regulation, FDA review, model transparency, childhood cancer, hematopoietic stem cell transplantation, AI governance, artificial intelligence in healthcare
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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