Cited by 172Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

External validation of AI models in health should be replaced with recurring local validation

Youssef, Alexey, Pencina, Michael, Thakur, Anshul, Zhu, Tingting, Clifton, David, Shah, Nigam H.·2023·Nature Medicine

DOI 10.1038/s41591-023-02540-z

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1038/s41591-023-02540-z

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

What Is Value in Health Care?
Porter, M. E. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1011024 · Cited by 5,084
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
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 477
Making Machine Learning Models Clinically Useful
Shah, N. H., Milstein, A., & Bagley, P. (2019) · JAMA · doi:10.1001/jama.2019.10306 · Cited by 294
Self-citation
There 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
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
Self-citation
The 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
Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database
Singh, H., Mhasawade, V., & Chunara, R. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000023 · Cited by 67
Towards Regulatory-Compliant MLOps: Oravizio’s Journey from a Machine Learning Experiment to a Deployed Certified Medical Product
Granlund, T., Stirbu, V., & Mikkonen, T. (2021) · SN Computer Science · doi:10.1007/s42979-021-00726-1 · Cited by 53

Show all 9 references

Research works printed but not linked: 2

Shankar, S., Garcia, R., Hellerstein, J. M. & Parameswaran, A. G. Preprint at arXiv https://arxiv.org/abs/2209.09125 (2022). · arXiv
Youssef, A. et al. Preprint at medRxiv https://medrxiv.org/content/10.1101/2022.08.09.22278600v2 (2022). · DOI

Sorted by citations

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
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
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 324
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
Self-citation
A 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
Self-citation
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
Van 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
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
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
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
“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 88
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
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
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 52
Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease
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
Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices
Mehta, V., Komanduri, A., Bhadouriya, R. S., Mehta, V., Johnson, M. D., Shrestha, P., Nikolov, M. C., Jain, B., Shah, N. H., & Schulman, K. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02052-9 · Cited by 47
Self-citation
Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart Association
Khan, 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 45
Self-citation
A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America
Ong, 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 42
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation
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 44
Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals
Everson, J., Nong, P., & Richwine, C. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.49463 · Cited by 33
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
Multimodal Image Dataset for AI-Based Skin Cancer (MIDAS) Benchmarking
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
Minimizing bias when using artificial intelligence in critical care medicine
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
Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium
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 26
Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes
Cau, R., Pisu, F., Suri, J. S., Montisci, R., Gatti, M., Mannelli, L., Gong, X., & Saba, L. (2024) · Diagnostics · doi:10.3390/diagnostics14020156 · Cited by 25
Prospective Human Validation of Artificial Intelligence Interventions in Cardiology
Moosavi, A., Huang, S., Vahabi, M., Motamedivafa, B., Tian, N., Mahmood, R., Liu, P. X., & Sun, C. L. F. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101202 · Cited by 29
Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart Association
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
Self-citation
Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery
Liu, 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 26
Randomized Controlled Trials Evaluating Artificial Intelligence in Cardiovascular Care
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 23
Generative Artificial Intelligence in Clinical Medicine and Impact on Gastroenterology
Soroush, A., Giuffrè, M., Chung, S., & Shung, D. L. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.03.038 · Cited by 23
Toward governance of artificial intelligence in pediatric healthcare
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 27
Development and Validation of a Risk Model to Predict Intraoperative Blood Transfusion
Eyth, A., Borngaesser, F., Rudolph, M. I., Paschold, B., Ramishvili, T., Kaiser, L., Tam, C. W., Wongtangman, K., Eikermann, G. M., Garg, S., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.5522 · Cited by 22
Interpretable inflammation landscape of circulating immune cells
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 23
Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances
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 20
Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model
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 18
TARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record
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
Artificial intelligence in drug discovery — what it is, where we stand and the path forward
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 23
The performance evaluation of artificial intelligence ERNIE bot in Chinese National Medical Licensing Examination
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
Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises
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
A Federated Registration System for Artificial Intelligence in Health
Pencina, M. J., McCall, J., & Economou-Zavlanos, N. J. (2024) · JAMA · doi:10.1001/jama.2024.14026 · Cited by 15
Self-citation
Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery
Landolfo, 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
Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT
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 17
Development and external validation of multimodal postoperative acute kidney injury risk machine learning models
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 15
The Need for Continuous Evaluation of Artificial Intelligence Prediction Algorithms
Shah, N. H., Pfeffer, M. A., & Ghassemi, M. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.33009 · Cited by 12
Self-citation
Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review
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
A multimodal framework for extraction and fusion of satellite images and public health data
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
External Validation of AI Models for Skin Diseases: A Systematic Review
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
Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up
Negoi, I. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i18.106670 · Cited by 11
Implementation of Artificial Intelligence–Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study
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 12
Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients
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
The Enemies of Reliable and Useful Clinical Prediction Models: A Review of Statistical and Scientific Challenges
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 14
Regulating Flexibility for Artificial Intelligence FDA Experience with Predetermined Change Control PlansPreprint
Rosen, K. L., & Mandl, K. D. (2025) · medRxiv · doi:10.1101/2025.08.26.25334477 · Cited by 13
A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models
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 11
Against reflexive recalibration: towards a causal framework for addressing miscalibration
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
Self-citation
Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies
Faviez, 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
Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study
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
Diversity and inclusion: A hidden additional benefit of Open Data
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
Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules
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 11
Artificial Intelligence‐Based Virtual Assistant for the Diagnostic Approach of Chronic Ataxias
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
The Integration of Multi-omics With Artificial Intelligence in Hepatology: A Comprehensive Review of Personalized Medicine, Biomarker Identification, and Drug Discovery
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
A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation
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 12
Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic ReviewPreprint
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
Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty
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
The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review
Mikhail, D., Milad, D., Antaki, F., Hammamji, K., Qian, C. X., Rezende, F., & Duval, R. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100689 · Cited by 7
The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis
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
AI-Driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism
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 8
Using large language models for temporal relation extraction from pediatric clinical reports
Andrew, J. J., Potier, J., Garcelon, N., Burgun, A., & Vincent, M. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf121 · Cited by 7
Bridging the Gap or Widening the Divide: A Call for Capacity-Building in Artificial Intelligence for Healthcare in the Philippines
Gutierrez, K. L. T., & Viacrusis, P. M. (2023) · Journal of Medicine University of Santo Tomas · doi:10.35460/2546-1621.2023-0081 · Cited by 8
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis
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 9
The role of artificial intelligence in cardiovascular research: Fear less and live bolder
Scuricini, A., Ramoni, D., Liberale, L., Montecucco, F., & Carbone, F. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14364 · Cited by 9
Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation
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 9
From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization
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
Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law
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
Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions
Rennie, O. (2024) · Medicine Health Care and Philosophy · doi:10.1007/s11019-024-10206-x · Cited by 7
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
Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models
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
Current challenges and the way forwards for regulatory databases of artificial intelligence as a medical device
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 8
Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar
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
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 6
Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study
Khumalo, A. C., & Alaqra, A. S. (2026) · JMIR Human Factors · doi:10.2196/83240 · Cited by 7
Interpretable machine learning model for early prediction of acute kidney injury in patients with rhabdomyolysis
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
Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging
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
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 8
A Comprehensive Review of Portable Retinal Cameras: Technical Features, AI Integration, and Clinical Potential
Nakayama, L., Ribeiro, L. Z., Tabuse, C., Malerbi, F., & Regatieri, C. (2025) · AJO International · doi:10.1016/j.ajoint.2025.100194 · Cited by 4
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
Expert-Based, Institutional Approaches for Reducing the Diagnostic Odyssey of Patients With Inborn Errors of Immunity
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
Regulating Generative AI in Radiology Practice: A Trilaminar Approach to Balancing Risk with Innovation
Gowda, V., Bizzo, B. C., & Dreyer, K. J. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.05.032 · Cited by 4
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
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
Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa
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
Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures
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
Leveraging Stacked Classifiers for Multi-task Executive Function in Schizophrenia Yields Diagnostic and Prognostic Insights
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 5
External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older
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
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
Closing the diagnostic gap: A narrative review of recent advances in functional MRI diagnostics in spinal cord injury
Entenmann, C. J., Kersting, K., Vajkoczy, P., & Zdunczyk, A. (2025) · Brain and Spine · doi:10.1016/j.bas.2025.104283 · Cited by 3
The Emerging Role of ISO 42001 Certification in Fostering the Deployment of Responsible Generative AI Healthcare SolutionsPreprint
Thiers, F. A., & Harned, Z. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5ks37 · Cited by 6
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
Big Data–Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review
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
A Review of Research on the Applications of Large Models in Each Functional Module of the Entire Rehabilitation Process
Bai, T., Jiang, K., Yu, Y., Qie, S., Wang, C., Wang, B., & Zhang, W. (2026) · Future Internet · doi:10.3390/fi18020095 · Cited by 2
Healthcare AI as Critical Digital Health Infrastructure: A Public Health Preparedness Framework for Systemic Risk
Lipskiy, N., & Flowerday, S. (2026) · Future Internet · doi:10.3390/fi18050232 · Cited by 2
Artificial intelligence–driven predictive modeling and smart surveillance systems for mycotoxin risk management in global food chains
Adeyeye, S. A. O. (2026) · Journal of Stored Products Research · doi:10.1016/j.jspr.2026.103138 · Cited by 2
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

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
ORCID: Youssef, Alexey
Pencina, Michael
Thakur, Anshul
Zhu, Tingting
Clifton, David
Shah, Nigam H.
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2023-10-18
Volume: 29
Issue: 11
Pages: 2686-2687
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-023-02540-z
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › 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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

External validation of AI models in health should be replaced with recurring local validation

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks