{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:17:27Z","timestamp":1784607447478,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02615-4","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T02:11:37Z","timestamp":1775527897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The portability paradox of foundation models for clinical decision support"],"prefix":"10.1038","volume":"9","author":[{"given":"Kyra L.","family":"Rosen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margaret","family":"Sui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel Yuhan","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph C.","family":"Kvedar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"2615_CR1","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1038\/s41746-022-00646-1","volume":"5","author":"JS Hinson","year":"2022","unstructured":"Hinson, J. S. et al. Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions. NPJ Digit. Med. 5, 94, https:\/\/doi.org\/10.1038\/s41746-022-00646-1 (2022).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR2","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1097\/CCM.0000000000005492","volume":"50","author":"CJ Winslow","year":"2022","unstructured":"Winslow, C. J. et al. The impact of a machine learning early warning score on hospital mortality: a multicenter clinical intervention trial. Crit. Care. Med. 50, 1339\u20131347, https:\/\/doi.org\/10.1097\/CCM.0000000000005492 (2022).","journal-title":"Crit. Care. Med."},{"key":"2615_CR3","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1038\/s41591-025-03609-7","volume":"31","author":"SC Rossetti","year":"2025","unstructured":"Rossetti, S. C. et al. Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial. Nat. Med. 31, 1895\u20131902, https:\/\/doi.org\/10.1038\/s41591-025-03609-7 (2025).","journal-title":"Nat. Med."},{"key":"2615_CR4","doi-asserted-by":"publisher","unstructured":"Bommasani, R., et al. On the opportunities and risks of foundation models. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2108.07258 (2021).","DOI":"10.48550\/arXiv.2108.07258"},{"key":"2615_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","volume":"3","author":"R Sutton","year":"2020","unstructured":"Sutton, R. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit. Med. 3, 17, https:\/\/doi.org\/10.1038\/s41746-020-0221-y (2020).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR6","doi-asserted-by":"publisher","first-page":"S101","DOI":"10.7326\/M19-0875","volume":"172","author":"K Kawamoto","year":"2020","unstructured":"Kawamoto, K. & McDonald, C. J. Designing, conducting, and reporting clinical decision support studies: recommendations and call to action. Ann. Intern. Med 172, S101\u2013S109, https:\/\/doi.org\/10.7326\/M19-0875 (2020).","journal-title":"Ann. Intern. Med"},{"key":"2615_CR7","doi-asserted-by":"publisher","unstructured":"Talianu, A. et al. Artificial intelligence for improving decision-making in bacterial infection management: a narrative review. J. Antimicrob. Chemother. 81, https:\/\/doi.org\/10.1093\/jac\/dkaf470 (2026).","DOI":"10.1093\/jac\/dkaf470"},{"key":"2615_CR8","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1056\/NEJMra2301725","volume":"388","author":"P Rajpurkar","year":"2023","unstructured":"Rajpurkar, P. & Lungren, M. P. The current and future state of AI interpretation of medical images. N. Eng. J. Med. 388, 1981\u20131990, https:\/\/doi.org\/10.1056\/NEJMra2301725 (2023).","journal-title":"N. Eng. J. Med."},{"key":"2615_CR9","doi-asserted-by":"publisher","first-page":"e2513685","DOI":"10.1001\/jamanetworkopen.2025.13685","volume":"8","author":"V Subasri","year":"2025","unstructured":"Subasri, V. et al. Detecting and remediating harmful data shifts for the responsible deployment of clinical AI models. JAMA Netw. Open 8, e2513685, https:\/\/doi.org\/10.1001\/jamanetworkopen.2025.13685 (2025).","journal-title":"JAMA Netw. Open"},{"key":"2615_CR10","doi-asserted-by":"publisher","unstructured":"Gallifant, J. et al. A field guide to deploying AI agents in clinical practice. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2509.26153 (2026).","DOI":"10.48550\/arXiv.2509.26153"},{"key":"2615_CR11","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41746-023-00879-8","volume":"6","author":"M Wornow","year":"2023","unstructured":"Wornow, M. et al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit. Med. 6, 135, https:\/\/doi.org\/10.1038\/s41746-023-00879-8 (2023).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR12","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy, L., Xiang, Y. & Xie, Z. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit. Med. 4, 86, https:\/\/doi.org\/10.1038\/s41746-021-00455-y (2021).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR13","doi-asserted-by":"publisher","unstructured":"Steinberg, E., Fries, J., Xu, Y. & Shah, N. MOTOR: a time-to-event foundation model for structured medical records. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2301.03150 (2025).","DOI":"10.48550\/arXiv.2301.03150"},{"key":"2615_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-024-01166-w","volume":"7","author":"LL Guo","year":"2024","unstructured":"Guo, L. L. et al. A multi-center study on the adaptability of a shared foundation model for electronic health records. NPJ Digit. Med. 7, 171, https:\/\/doi.org\/10.1038\/s41746-024-01166-w (2024).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J. et al. Segment anything in medical images. Nat. Commun. 15, 654. https:\/\/doi.org\/10.1038\/s41467-024-44824-z (2024).","journal-title":"Nat. Commun."},{"key":"2615_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-026-02202-6","author":"D Tak","year":"2026","unstructured":"Tak, D. et al. A generalizable foundation model for analysis of human brain MRI. Nat. Neurosci. https:\/\/doi.org\/10.1038\/s41593-026-02202-6 (2026).","journal-title":"Nat. Neurosci."},{"key":"2615_CR17","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1038\/s41591-025-04133-4","volume":"32","author":"R Thapa","year":"2026","unstructured":"Thapa, R. et al. A multimodal sleep foundation model for disease prediction. Nat Med 32, 752\u2013762, https:\/\/doi.org\/10.1038\/s41591-025-04133-4 (2026).","journal-title":"Nat Med"},{"key":"2615_CR18","doi-asserted-by":"publisher","first-page":"e24296","DOI":"10.1148\/radiol.242961","volume":"317","author":"N Tavakoli","year":"2025","unstructured":"Tavakoli, N. et al. Generative AI and foundation models in radiology: applications, opportunities, and potential challenges. Radiology 317, e24296, https:\/\/doi.org\/10.1148\/radiol.242961 (2025).","journal-title":"Radiology"},{"key":"2615_CR19","doi-asserted-by":"publisher","unstructured":"Bolton, E. et al. BioMedLM: A 2.7B parameter language model trained on biomedical text. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2403.18421 (2025).","DOI":"10.48550\/arXiv.2403.18421"},{"key":"2615_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-30820-8","volume":"13","author":"LL Guo","year":"2023","unstructured":"Guo, L. L. et al. EHR foundation models improve robustness in the presence of temporal distribution shift. Sci. Rep. 13, 3767. https:\/\/doi.org\/10.1038\/s41598-023-30820-8 (2023).","journal-title":"Sci. Rep."},{"key":"2615_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43715-z","volume":"14","author":"Z Yang","year":"2023","unstructured":"Yang, Z. et al. TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nat. Commun. 14, 7857. https:\/\/doi.org\/10.1038\/s41467-023-43715-z (2023).","journal-title":"Nat. Commun."},{"key":"2615_CR22","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1038\/s41586-025-09529-3","volume":"647","author":"A Shmatko","year":"2025","unstructured":"Shmatko, A. et al. Learning the natural history of human disease with generative transformers. Nature 647, 248\u2013256, https:\/\/doi.org\/10.1038\/s41586-025-09529-3 (2025).","journal-title":"Nature"},{"key":"2615_CR23","doi-asserted-by":"publisher","unstructured":"Wornow, M. et al. EHRSHOT: an EHR benchmark for few-shot evaluation of foundation models. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2307.02028 (2023).","DOI":"10.48550\/arXiv.2307.02028"},{"key":"2615_CR24","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/s41746-026-02337-7","volume":"9","author":"S Yakdan","year":"2026","unstructured":"Yakdan, S. et al. Clinically-guided models or foundation models? Predicting cervical spondylotic myelopathy from electronic health records. NPJ Digit. Med. 9, 153, https:\/\/doi.org\/10.1038\/s41746-026-02337-7 (2026).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR25","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jacc.2020.11.030","volume":"77","author":"G Quer","year":"2021","unstructured":"Quer, G., Arnaout, R., Henne, M. & Arnaout, R. Machine learning and the future of cardiovascular care: JACC state-of-the-art review. J. Am. Coll. Cardiol 77, 300\u2013313, https:\/\/doi.org\/10.1016\/j.jacc.2020.11.030 (2021).","journal-title":"J. Am. Coll. Cardiol"},{"key":"2615_CR26","doi-asserted-by":"publisher","first-page":"901428","DOI":"10.3389\/fninf.2022.901428","volume":"16","author":"S Suresh","year":"2022","unstructured":"Suresh, S., Newton, D. T., Everett, T. H. IV, Lin, G. & Duerstock, B. S. Feature selection techniques for a machine learning model to detect autonomic dysreflexia. Front. Neuroinform. 16, 901428, https:\/\/doi.org\/10.3389\/fninf.2022.901428 (2022).","journal-title":"Front. Neuroinform."},{"key":"2615_CR27","doi-asserted-by":"publisher","first-page":"927312","DOI":"10.3389\/fbinf.2022.927312","volume":"2","author":"N Pudjihartono","year":"2022","unstructured":"Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O\u2019Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinform 2, 927312, https:\/\/doi.org\/10.3389\/fbinf.2022.927312 (2022).","journal-title":"Front. Bioinform"},{"key":"2615_CR28","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41746-025-01438-z","volume":"8","author":"B Theodorou","year":"2025","unstructured":"Theodorou, B. et al. Improving medical machine learning models with generative balancing for equity and excellence. NPJ Digit. Med. 8, 100, https:\/\/doi.org\/10.1038\/s41746-025-01438-z (2025).","journal-title":"NPJ Digit. Med."},{"key":"2615_CR29","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1038\/s41592-023-01886-z","volume":"20","author":"J Banerjee","year":"2023","unstructured":"Banerjee, J. et al. Machine learning in rare disease. Nat. Methods 20, 803\u2013814, https:\/\/doi.org\/10.1038\/s41592-023-01886-z (2023).","journal-title":"Nat. Methods"},{"key":"2615_CR30","doi-asserted-by":"publisher","first-page":"e2032669","DOI":"10.1001\/jamanetworkopen.2020.32669","volume":"4","author":"H Allen","year":"2021","unstructured":"Allen, H., Gordon, S. H., Lee, D., Bhanja, A. & Sommers, B. D. Comparison of utilization, costs, and quality of Medicaid vs subsidized private health insurance for low-income adults. JAMA Netw. Open 4, e2032669, https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.32669 (2021).","journal-title":"JAMA Netw. Open"},{"key":"2615_CR31","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1001\/jamainternmed.2020.5408","volume":"180","author":"S Swaminathan","year":"2020","unstructured":"Swaminathan, S., Ndumele, C. D., Gordon, S. H., Lee, Y. & Trivedi, A. N. Association of Medicaid-focused or commercial Medicaid managed care plan type with outpatient and acute care. JAMA Intern. Med. 180, 1672\u20131679, https:\/\/doi.org\/10.1001\/jamainternmed.2020.5408 (2020).","journal-title":"JAMA Intern. Med."},{"key":"2615_CR32","unstructured":"Waxler, S. et al. Generative Medical Event Models Improve with Scale. Preprint at https:\/\/arxiv.org\/abs\/2508.12104 (2025)."},{"key":"2615_CR33","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1056\/NEJMsr1110507","volume":"365","author":"D Blumenthal","year":"2011","unstructured":"Blumenthal, D. Wiring the health system\u2014origins and provisions of a new federal program. N. Eng. J. Med. 365, 2323\u20132329, https:\/\/doi.org\/10.1056\/NEJMsr1110507 (2011).","journal-title":"N. Eng. J. Med."},{"key":"2615_CR34","unstructured":"U.S. Food and Drug Administration. 510(k) Summary: BriefCase-Triage: CARE Multi-triage CT Body (K252970). https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf25\/K252970.pdf (2026)."},{"key":"2615_CR35","doi-asserted-by":"publisher","first-page":"e618","DOI":"10.1016\/S2589-7500(23)00126-7","volume":"5","author":"UJ Muehlematter","year":"2023","unstructured":"Muehlematter, U. J., Bluethgen, C. & Vokinger, K. N. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit. Health 5, e618\u2013e626, https:\/\/doi.org\/10.1016\/S2589-7500(23)00126-7 (2023).","journal-title":"Lancet Digit. Health"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02615-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02615-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02615-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T02:11:41Z","timestamp":1775527901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02615-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,7]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2615"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02615-4","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,7]]},"assertion":[{"value":"21 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"K.R., A.O.Y. and M.S. declare no financial or non-financial competing interests. J.K. serves as Editor-in-Chief of this journal and had no role in the peer-review or decision to publish this manuscript. J.K. declares no financial competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"285"}}