{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T08:20:08Z","timestamp":1778142008000,"version":"3.51.4"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000292","name":"Cystic Fibrosis Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000292","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002066","name":"GlaxoSmithKline foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-024-01127-3","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T10:02:14Z","timestamp":1716285734000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Generalization\u2014a key challenge for responsible AI in patient-facing clinical applications"],"prefix":"10.1038","volume":"7","author":[{"given":"Lea","family":"Goetz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2134-6639","authenticated-orcid":false,"given":"Nabeel","family":"Seedat","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Vandersluis","sequence":"additional","affiliation":[]},{"given":"Mihaela","family":"van der Schaar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"1127_CR1","doi-asserted-by":"crossref","unstructured":"Vandersluis, R. & Savulescu, J. The selective deployment of AI in healthcare: An ethical algorithm for algorithms. Bioethics 38, 391\u2013400 (2024).","DOI":"10.1111\/bioe.13281"},{"key":"1127_CR2","first-page":"1","volume":"23","author":"A D\u2019Amour","year":"2022","unstructured":"D\u2019Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. J. Mach. Learn. Res. 23, 1\u201361 (2022).","journal-title":"J. Mach. Learn. Res."},{"key":"1127_CR3","unstructured":"Gulrajani, I. & Lopez-Paz, D. In Search of Lost Domain Generalization. In International Conference on Learning Representations (2020)."},{"key":"1127_CR4","doi-asserted-by":"crossref","unstructured":"Seedat, N., Imrie, F. & van der Schaar, M. Navigating Data-Centric Artificial Intelligence with DC-Check: Advances, Challenges, and Opportunities. IEEE Transactions on Artificial Intelligence (2023).","DOI":"10.1109\/TAI.2023.3345805"},{"key":"1127_CR5","doi-asserted-by":"publisher","first-page":"e489","DOI":"10.1016\/S2589-7500(20)30186-2","volume":"2","author":"J Futoma","year":"2020","unstructured":"Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Digital Health 2, e489\u2013e492 (2020).","journal-title":"Lancet Digital Health"},{"key":"1127_CR6","unstructured":"Tran, D. et al. Plex: Towards reliability using pretrained large model extensions. arXiv preprint arXiv:2207.07411 (2022)."},{"key":"1127_CR7","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1038\/s41591-021-01312-x","volume":"27","author":"E Wu","year":"2021","unstructured":"Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582\u2013584 (2021).","journal-title":"Nat. Med."},{"key":"1127_CR8","doi-asserted-by":"crossref","unstructured":"Ferzoco, R. M. & Ruddy, K. J. Optimal delivery of male breast cancer follow-up care: improving outcomes. Breast Cancer: Targets Ther. 371-379 (2015).","DOI":"10.2147\/BCTT.S75630"},{"key":"1127_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5152\/tjbh.2015.2711","volume":"12","author":"M Yalaza","year":"2016","unstructured":"Yalaza, M., \u0130nan, A. & Bozer, M. Male breast cancer. J. Breast Health 12, 1 (2016).","journal-title":"J. Breast Health"},{"key":"1127_CR10","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1038\/s42256-021-00353-8","volume":"3","author":"AM Alaa","year":"2021","unstructured":"Alaa, A. M., Gurdasani, D., Harris, A. L., Rashbass, J. & van der Schaar, M. Machine learning to guide the use of adjuvant therapies for breast cancer. Nat. Mach. Intell. 3, 716\u2013726 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"1127_CR11","unstructured":"Parfit, D. Equality and priority. Ratio (2002)."},{"key":"1127_CR12","doi-asserted-by":"publisher","first-page":"102274","DOI":"10.1016\/j.media.2021.102274","volume":"75","author":"AG Roy","year":"2022","unstructured":"Roy, A. G. et al. Does your dermatology classifier know what it doesn\u2019t know? detecting the long-tail of unseen conditions. Med. Image Anal. 75, 102274 (2022).","journal-title":"Med. Image Anal."},{"key":"1127_CR13","unstructured":"Jaeger, P. F., L\u00fcth, C. T., Klein, L. & Bungert, T. J. A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification. In The Eleventh International Conference on Learning Representations (2022)."},{"key":"1127_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41523-022-00448-4","volume":"8","author":"R Yoder","year":"2022","unstructured":"Yoder, R. et al. Impact of low versus negative estrogen\/progesterone receptor status on clinico-pathologic characteristics and survival outcomes in HER2-negative breast cancer. NPJ Breast Cancer 8, 80 (2022).","journal-title":"NPJ Breast Cancer"},{"key":"1127_CR15","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1038\/s42256-022-00516-1","volume":"4","author":"W Liang","year":"2022","unstructured":"Liang, W. et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4, 669\u2013677 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"1127_CR16","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41588-021-00961-5","volume":"54","author":"Y Ding","year":"2022","unstructured":"Ding, Y. et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat. Genet. 54, 30\u201339 (2022).","journal-title":"Nat. Genet."},{"key":"1127_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87762-2","volume":"11","author":"S Tang","year":"2021","unstructured":"Tang, S. et al. Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset. Sci. Rep. 11, 8366 (2021).","journal-title":"Sci. Rep."},{"key":"1127_CR18","doi-asserted-by":"crossref","unstructured":"Song, H., Kim, M., Park, D., Shin, Y. & Lee, J. G. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022).","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"1127_CR19","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J. et al. A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56, 1513\u20131589 (2023).","journal-title":"Artif. Intell. Rev."},{"key":"1127_CR20","unstructured":"Vovk, V., Gammerman, A. & Shafer, G. Algorithmic learning in a random world Vol. 29 (Springer, New York, 2005)."},{"key":"1127_CR21","unstructured":"Salehi, M. et al. A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges. Transact. Mach. Learn. Res. (2022)."},{"key":"1127_CR22","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1038\/s41591-020-0842-3","volume":"26","author":"Y Liu","year":"2020","unstructured":"Liu, Y. et al. A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26, 900\u2013908 (2020).","journal-title":"Nat. Med."},{"key":"1127_CR23","first-page":"22221","volume":"34","author":"B Van Breugel","year":"2021","unstructured":"Van Breugel, B., Kyono, T., Berrevoets, J. & Van der Schaar, M. Decaf: Generating fair synthetic data using causally-aware generative networks. Adv. Neural Inf. Process. Syst. 34, 22221\u201322233 (2021).","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01127-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01127-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01127-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T10:12:41Z","timestamp":1716286361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01127-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1127"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01127-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]},"assertion":[{"value":"7 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"L.G. and R.V. are employees of GSK. N.S. is funded by the Cystic Fibrosis Trust.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"126"}}