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

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

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

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

Artificial Intelligence in Biosensor Systems for Healthcare: From Molecular Recognition to Machine Learning

Altıntaş, Ö., & Deni̇zli̇, A. (2026) · Electronics · doi:10.3390/electronics15153388 · Cited by 2

Deep learning to predict fetal acidemia: a response

McCoy, J. A., & Cava, W. L. (2024) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.07.037 · Cited by 2

The potential risks of artificial intelligence in cardiovascular care

Göçer, H., & Durukan, A. B. (2025) · Turkish Journal of Thoracic and Cardiovascular Surgery · doi:10.5606/tgkdc.dergisi.2025.26912 · Cited by 2

Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Were, M. C., Li, A., Malin, B., Yin, Z., Coco, J., Collins, B. X., Clayton, E. W., Novak, L. L., Hendricks‐Sturrup, R., Oluyomi, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73996 · Cited by 4

AI ethics for the everyday intensivist

Tan, S. C., Modra, L. J., & Hensman, T. (2025) · Critical Care and Resuscitation · doi:10.1016/j.ccrj.2025.100115 · Cited by 2

Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India

Jagadeesh, A. T., Aramrat, C., Rai, S., Maqsood, F. H., Madhukeshwar, A. K., Bhogadi, S., Lieber, J., Mahajan, H., Banjara, S. K., Lewin, A., et al. (2025) · The Lancet Regional Health - Southeast Asia · doi:10.1016/j.lansea.2025.100644 · Cited by 2

A Distinct Approach to Clinical GenAI OversightPreprint

Thiers, F. A., & Lucy, K. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/vm6zy · Cited by 2

Artificial intelligence in breast cancer: clinical applications in diagnosis, prognosis, and therapeutics

Singh, J., Alsaidan, O. A., Aodah, A. H., Alrobaian, M., Almalki, W. H., Almujri, S. S., Sahoo, A., Alam, K., Lal, J. A., Barkat, M. A., et al. (2025) · Future Oncology · doi:10.1080/14796694.2025.2606642 · Cited by 9

Characterizing patients who benefit from mature medical AI models in real-world clinical applications

Chen, Z., Li, W., & Lin, Z. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001283 · Cited by 5

Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease

Moldakhmetova, S., Bimurat, B., Berdaly, A., Makhammajanov, Z., Salykov, A., Bukasov, R., & Gaipov, A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27083648 · Cited by 1

Automated Triage for New Keratoconus Referrals Using Multimodal Deep Learning

Balal, S., Kandakji, L., Leucci, M. T., Keane, P. A., Gore, D. M., Pontikos, N., & Allan, B. D. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101208 · Cited by 1

From forensic medical databases to court-defensible artificial intelligence: the next translational step is standards, not scale

Vijayasimha, M., Dubey, A. K., Yaqoob, S. I., & Charan, L. S. (2026) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-026-01256-z · Cited by 1

Externally Tested AI Models for Malignancy Classification of Lung Nodules at CT: A Systematic Review and Meta-Analysis

Asmara, O. D., Steenhuis, E. G. M., de Jong, K., Joseph, A., Timmer, M., Tenda, E. D., Boerma, E. C., Heuvelmans, M. A., & van Geffen, W. H. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250331 · Cited by 3

Integration of artificial intelligence applications in clinical pharmacy services: A scoping review

Al‐Taie, A., Abdullah, A. S., & Arueyingho, O. (2026) · Future Healthcare Journal · doi:10.1016/j.fhj.2026.100547 · Cited by 1

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Catling, F. J. R., Nagendran, M., Festor, P., Bien, Z., Harris, S. K., Faisal, A. A., Gordon, A. C., & Komorowski, M. (2024) · Critical Care Explorations · doi:10.1097/cce.0000000000001087 · Cited by 1

An Explainable AI Framework for Neonatal Mortality Risk Prediction in Kenya: Enhancing Clinical Decisions with Machine Learning

Lumumba, V. W., Muriithi, D. K., Njoroge, E. W., Langat, A. K., Mwebesa, E., & Wanyama, M. (2025) · Biomedical Statistics and Informatics · doi:10.11648/j.bsi.20251003.12 · Cited by 1

Health AI Assurance Laboratories—Reply

Shah, N. H., Halamka, J. D., & Anderson, B. (2024) · JAMA · doi:10.1001/jama.2024.1087 · Cited by 1

Hazard-aware adaptations bridge the generalization gap in large language models: a nationwide studyPreprint

Wu, J. T.-Y., Conover, S., Su, C. C., Corrigan, J., Culnan, J. M., Liu, Y., Kelley, M., Do, N., Arya, S., Harris, A. H. S., et al. (2025) · medRxiv · doi:10.1101/2025.02.14.25322312 · Cited by 1

Combining multimorbidity clustering with limited demographic information enables high-precision outcome predictionsPreprint

Ferreira, F. S., Lannou, E. L., Post, B., Haar, S., Kadirvelu, B., Brett, S. J., & Faisal, A. A. (2024) · medRxiv · doi:10.1101/2024.05.28.24308024

A validated model for early prediction of group A streptococcal aetiology and clinical endpoints in necrotising soft tissue infectionsPreprint

Katz, S., Suijker, J., Skrede, S., Vries, A. M., Pijpe, A., Norrby‐Teglund, A., Medina, L. M. P., Damås, J. K., Hyldegaard, O., Solligård, E., et al. (2024) · medRxiv · doi:10.1101/2024.06.05.24308478

Diagnostic Accuracy of Convolutional Neural Networks in Classifying Hepatic Steatosis from B-Mode Ultrasound Images: A Systematic Review with Meta-Analysis and Novel Validation in a Community Setting in South IndiaPreprint

Jagadeesh, A., Aramrat, C., Rai, S., Maqsood, F. H., Madhukeshwar, A. K., Bhogadi, S., Lieber, J., Mahajan, H., Banjara, S. K., Lewin, A., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153451

Response to the Letter to the Editor: “AI-Assisted IBD Severity Classification: A Critical Perspective on Current Evidence and Next Steps”

Lee, M. C. M., Farahvash, A., & Zezos, P. (2025) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaf096

Generalizability of Risk Models for Treatment-Resistant Depression Across Three Health SystemsPreprint

Walsh, C. G., Ripperger, M. A., McCoy, T. H., Castro, V. M., Hu, Y., Kirchner, H. L., Ruderfer, D. M., & Perlis, R. H. (2025) · medRxiv · doi:10.1101/2025.05.21.25328089

Artificial intelligence in thoracic pathology: diagnostic and predictive applications

von der Thüsen, J. H. (2025) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2025.06.008

Portability of an artificial intelligence model for self-harm detection across hospital settingsPreprint

Rozova, V. S., Witt, K., Conway, M., Robinson, J., & Verspoor, K. M. (2025) · medRxiv · doi:10.1101/2025.07.10.25331160

Predictive Performance Precision Analysis in Medicine: Identification of low-confidence predictions at patient and profile levels (MED3pa I)Preprint

Lefebvre, O., Lemyre, F. C., Éthier, J., Chikouche, L. H., Amriou, L., Poenaru, D., & Vallières, M. (2025) · medRxiv · doi:10.1101/2025.08.22.25334254

Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach

Wang, X., Jahagirdar, S., Kemp, B., Gross, J. J., Bruckmaier, R. M., Saccenti, E., & van Knegsel, A. T. M. (2025) · Metabolites · doi:10.3390/metabo15110698

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients

Yan, A. P., Guo, L., Patel, P., Schechter, T., Arciniegas, S. E., Inoue, J., Vettese, E., Jessa, K., Cardiff, B., TOMLINSON, G. A., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-15137-1

Multimorbidity measurement strategies for predicting hospital visits

Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z

Learning Patient Similarity from Genomics for Precision OncologyPreprint

Shady, M., Reardon, B. M., Jiang, S., Pimenta, E. M., O’Meara, T. A., Park, J., Kehl, K. L., Elmarakeby, H., Sunyaev, S. R., & Allen, E. M. V. (2025) · medRxiv · doi:10.64898/2025.12.17.25342480

Artificial intelligence for enhancing decision-making in multidisciplinary tumor boards for HCC in China

Li, D., Wang, H., Gao, F., Fu, X., & Han, J. (2025) · Hepatoma Research · doi:10.20517/2394-5079.2025.67

Additional file 2 of 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) · Figshare · doi:10.6084/m9.figshare.25902422

Additional file 1 of 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) · Figshare · doi:10.6084/m9.figshare.25902330

A validated model for early prediction of group A streptococcal aetiology in necrotising soft tissue infections using minimal patient data

Katz, S., Suijker, J., Skrede, S., Vries, A. M., Pijpe, A., Norrby‐Teglund, A., Medina, L. M. P., Damås, J. K., Hyldegaard, O., Solligård, E., et al. (2026) · BMC Medicine · doi:10.1186/s12916-025-04593-y

Artificial Intelligence to Propel Evidence Generation

Dhruva, S. S., Curtis, L. H., & Califf, R. M. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.11.011

Artificial Intelligence Monitoring of Neurological Status From Patient Videos in the Neuroscience Intensive Care Unit

Feng, R., Richter, F., Mari, E., Gleason, A., Le, C., Kellner, C. P., Shrivastava, R. K., Fields, M. C., Rapoport, B. I., Bederson, J. B., et al. (2026) · Neurosurgery · doi:10.1227/neu.0000000000003899

Leveraging adaptive artificial intelligence and sensor fusion for enhanced process optimization and monitoring in chemical engineering

Abubakar, A. M., Ferhoune, I., Ndaba, S., Bilal, M. T., & Abdul, A. Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36412-9.00010-8

A comparative evaluation of time-series models for forecasting inpatient deaths and discharges against medical advice

Pang, C., Jiayong, D., Jiang, D., Wang, Y., Li, N., & Ren, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-37913-0

Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort

Michopoulou, S. K., Prosser, A. M. J., O’Brien, N., Dickson, J. C., Guy, M., Teeling, J. L., & Kipps, C. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251413790

Deep learning for estimating right ventricular function from routine coronary angiography

Rostami, B., Bhattarai, P., Al-Abcha, A. K., Lee, E., Chang, S.-S., Sarraf, M., Attia, Z. I., Fetterly, K. A., Thaden, J. J., Friedman, P. A., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag004

Advanced artificial intelligence vs simpler models for 1-year death prediction among patients receiving hemodialysis

Karthikeyan, K., Flythe, J. E., Pun, P. H., Winkelmayer­, W. C., & Carlson, D. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf152

Predictive performance precision analysis in medicine: identification of low-confidence predictions at patient and profile levels (MED3pa I)

Lefebvre, O., Lemyre, F. C., Éthier, J., Chikouche, L. H., Amriou, L., Poenaru, D., & Vallières, M. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag034 · Cited by 1

Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective

Dumitrascu, I., Zărnescu, N. O., Marchegiani, G., Ilie, A., Zarnescu, E. C., & Costea, R. (2026) · Diagnostics · doi:10.3390/diagnostics16091350

Executive function profiles identify stable schizophrenia subtypes with distinct symptoms, biological markers and treatment responses: a cross-time clustering and validation study

Zhang, T., Huo, X., Chen, J., & Zhao, X. (2026) · BMC Medicine · doi:10.1186/s12916-026-04955-0

Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer

Dor, B. B., & Sonis, S. T. (2026) · Head and Neck Pathology · doi:10.1007/s12105-026-01919-x

MHub.ai: A Standardized Platform for Reproducible AI Research in Medical ImagingPreprint

Aerts, H. J. W. L., Nürnberg, L., Bontempi, D., Pai, S., Lisle, C. R., Pieper, S., Kikinis, R., van de Leemput, S. C., Soni, R., Murugesan, G., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9480032/v1

Validating predictive models on EHRs data : lessons learnt from the NCDR-IMPACT-score applied on a French cardiac catheterization cohort

Quennelle, S., Malekzadeh-Milani, S. G., Garcelon, N., Burgun, A., Bonnet, D., & Neuraz, A. (2026) · BMC Cardiovascular Disorders · doi:10.1186/s12872-026-06065-4

Toward Effective Human Oversight in a Human-in-the-Loop AI System: Multi-Model AI and Model Labeling

Chang, J., Oh, Z. X., & Syafiie, S. (2026) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis69435.2026.11600358

Quality of real-world data as a condition for the transferability of medical artificial intelligence models

Abdulkerimova, S. F., & Danilova, Y. A. (2026) · Real-World Data & Evidence · doi:10.37489/2782-3784-myrwd-107

Cross-database validation reveals distinct layers of transportability in ICU delirium predictionPreprint

Ni, S., & Sato, K. (2026) · medRxiv · doi:10.64898/2026.07.19.26358409

Post-thrombectomy models for outcome prediction in ischemic stroke: systematic review and external validation

Li, X., Alexandrov, N., Veltman, Y., van Walsum, T., Bos, D., Majoie, C. B. L. M., van Zwam, W. H., Dippel, D. W. J., Roozenbeek, B., Lingsma, H. F., et al. (2026) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2026-025297

Systematic assessment of mixed imputation methods and explainable machine learning

Li, J.-Y., & Zhao, Y. (2026) · World Journal of Gastrointestinal Surgery · doi:10.4240/wjgs.v18.i7.119087

Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift

Pan, L., Zhao, G., Yu, Y., & Qin, G. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01285-x

Local retraining mitigates domain shift in sepsis prediction: Lessons from translating a neonatal model to mixed intensive care dataPreprint

Champeaux, S., Booth, J., Brown, A. P. Y., Sebire, N. J., Drobnjak, I., & Bowyer, S. A. (2026) · medRxiv · doi:10.64898/2026.08.18.26360666

Performance of MRI-based deep learning models in differentiation of triple negative breast cancer from other breast cancer subtypes: A systematic review and meta-analysis

Moradi, Z., Kiani, I., Rahmani, S. A. M. S., Mirzai, M., Elhaie, M., Dahaj, M. E., Rahimi, M., Mosadegh, M., Mohammadzadeh, S., & Gity, M. (2026) · European Journal of Radiology Open · doi:10.1016/j.ejro.2026.100808

Managing Uncertainty in AI-Enabled Occupational Injury Claims Review: An Observed-to-Expected Interpretation Framework (Preprint)Preprint

Wang, M., Li, J., Cheng, A. S. K., Lo, H. K. Y., & Ng, P. H. F. (2026) · doi:10.2196/preprints.108436

Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review

Huang, J., Ong, W., Zheng, A. L. W., Tan, T. S. E., Zheng, G. T. C., Teo, E. C., Tan, J. H., Kumar, N., & Hallinan, J. T. P. D. (2026) · Cancers · doi:10.3390/cancers18172794

External validation of a deep-learning based automatic contouring algorithm for the radius and tibia on first- and second-generation HR-pQCT scans

van den Bergh, F. J. P. N., van Rietbergen, B., van den Bergh, J. P., Biver, E., Wyers, C. E., & Bevers, M. S. A. M. (2026) · Bone · doi:10.1016/j.bone.2026.118076

Global research landscape, hotspots, and emerging trends of digital biomarkers: A bibliometric analysis

Tian, Y., Tian, X., Li, Z., Wang, Z., Guo, H., Meng, F., & Wang, Z. (2026) · Digital Health · doi:10.1177/20552076261487029

Exploratory task-specific evaluation of large language models in lung cancer clinical scenarios: A comparative study

Zhang, W., Li, X., Yuan, R., Zhang, Y., Yang, Y., Xu, Y., Zhong, R., Chen, S., Liu, D., Gao, L., et al. (2026) · Digital Health · doi:10.1177/20552076261487079

Consensus framework for the validation of generative AI: call for collaborators on the Validation Accords

Huo, B., Chartash, D., Tsang, L., Collins, P. G. S., Cacciamani, G. E., Ruurda, J. P. H., Mayol, J. M., Hinchliffe, R. J., & Guyatt, G. (2026) · Nature Medicine · doi:10.1038/s41591-026-04647-5

Predicting Psychological Flourishing Among Psychologists: Integrating Self-Compassion, Traditional Regression, and Machine Learning Approaches

Alhalawany, R. M., Alnufaie, R. F., & Khatatbeh, Y. M. (2026) · Healthcare · doi:10.3390/healthcare14182924

Temporal and cross-site validation of an AI system for self-harm detection

Rozova, V. S., Chen, L., Witt, K., Conway, D. M., Robinson, J., & Verspoor, K. M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001667

Responsible Implementation of EHR-Based Clinical Prediction Models—Beyond Performance

Zhao, S. J., & Deardorff, W. J. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.32906

Knowing When to Defer: Trustworthy Multimodal AI for BI-RADS-Derived Management Using Paired Mammography and Ultrasound

Nouman, M., & Haraguchi, R. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6050074

In silico biomedicine in the era of artificial intelligence: opportunities and challenges

Mustaffa, F.-E., Sajid, T., Ali, M., & Ashraf, N. M. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100629

Admission Glasgow Coma Scale Captures Most of the Measurable Prognostic Signal in Intracranial Suppurative Infections: An Explainable Machine-Learning Cohort Study

Teti, F. S. L., Keke, K. J.-B., Yao, K. S., Dongo, K. Y. S., Fionko, Y. B., & Haïdara, A. (2026) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v5i9.1664

AI Prognosis in Healthcare, Bias, Misprediction, and Governance Gaps

Düffelmeyer, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0403-6.ch008

Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support

Cappellani, F., Zeppieri, M., Antonio, L., Mazzotta, C. G., Gattazzo, I., D’Esposito, F., Gagliano, C., & Capobianco, M. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15207785

Cross-Dataset Wearable Sleep Staging under Source-Only and Target-Normalized Conditions

Fei, Y., Shen, C., & Xiao, F. (2026) · International Conference on Computer Network, Electronic and Automation (ICCNEA) · doi:10.1109/iccnea70678.2026.11722303

Rural Hospital AI Use and Evaluation

Cruikshank, J. A., Whitacre, B. E., & Davis, A. F. (2026) · The Journal of Rural Health · doi:10.1111/jrh.70237

AI algorithms and external validation: promises, pitfalls, and a practical blueprint

Thambiraj, G., & Armoundas, A. A. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag816

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

Medical AI in the Americas: policy, validation, and representativeness

Americas, T. L. R. H. –. (2026) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2026.101579 · Cited by 1

AI-based risk models as medical devices: how can we know they work?

CALSTER, B. V., Ensor, J., Barreñada, L., Perry, B. I., & Wynants, L. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20075552

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

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

External validation is not a bureaucratic detailPreprint

Egea, J. A. L. (2026) · doi:10.21428/36973002.dc9ea207