{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T10:09:31Z","timestamp":1776852571234,"version":"3.51.2"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of interest that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they inform high-stakes healthcare decisions. This position paper presents a consensus statement developed by experts within the In Silico World Community of Practice. We outline 12 key statements forming the theoretical foundation for evaluating the credibility of ML predictors, emphasizing the necessity of causal knowledge, rigorous error quantification, and robustness to biases. By comparing ML predictors with biophysical models, we highlight unique challenges associated with implicit causal knowledge and propose strategies to ensure reliability and applicability. Our recommendations aim to guide researchers, developers, and regulators in the rigorous assessment and deployment of ML predictors in clinical and biomedical contexts.<\/jats:p>","DOI":"10.1093\/bib\/bbaf100","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T08:07:01Z","timestamp":1740470821000},"source":"Crossref","is-referenced-by-count":3,"title":["Consensus statement on the credibility assessment of machine learning predictors"],"prefix":"10.1093","volume":"26","author":[{"given":"Alessandra","family":"Aldieri","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Politecnico di Torino , Corso Duca degli Abruzzi, 24 - 10129 Torino ,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiranja Prasad Babarenda","family":"Gamage","sequence":"additional","affiliation":[{"name":"Auckland Bioengineering Institute, University of Auckland , Private Bag 92019, Auckland 1142 - \u00a0","place":["New Zealand"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonino","family":"Amedeo La Mattina","sequence":"additional","affiliation":[{"name":"Medical Technology Laboratory, IRCCS Istituto Ortopedico Rizzoli , Via di Barbiano, 1\/10 - 40136 Bologna ,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University , 512 Huashan Rd, Jing'An, 200031, Shanghai ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axel","family":"Loewe","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT) , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen ,","place":["Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1668-3320","authenticated-orcid":false,"given":"Francesco","family":"Pappalardo","sequence":"additional","affiliation":[{"name":"Department of Drug and Health Sciences, University of Catania , V.le A. Doria, 6, 95125 Catania ,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Viceconti","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Alma Mater Studiorum\u2014University of Bologna , Via Zamboni, 33 - 40126 Bologna ,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"2026021923163249800_ref1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","article-title":"Machine learning for medical diagnosis: history, state of the art and perspective","volume":"23","author":"Kononenko","year":"2001","journal-title":"Artif Intell Med"},{"key":"2026021923163249800_ref2","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1146\/annurev.bioeng.8.061505.095802","article-title":"Machine learning for detection and diagnosis of disease","volume":"8","author":"Sajda","year":"2006","journal-title":"Annu Rev Biomed Eng"},{"key":"2026021923163249800_ref3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MEMB.2007.335579","article-title":"Machine learning in the life sciences","volume":"26","author":"Cios","year":"2007","journal-title":"IEEE Eng Med Biol Mag"},{"key":"2026021923163249800_ref4","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1016\/j.media.2012.02.005","article-title":"Machine learning and radiology","volume":"16","author":"Wang","year":"2012","journal-title":"Med Image Anal"},{"key":"2026021923163249800_ref5","doi-asserted-by":"publisher","first-page":"867924","DOI":"10.1155\/2013\/867924","article-title":"Machine learning approaches: from theory to application in schizophrenia","volume":"2013","author":"Veronese","year":"2013","journal-title":"Comput Math Methods Med"},{"key":"2026021923163249800_ref6","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"2026021923163249800_ref7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput Struct Biotechnol J"},{"key":"2026021923163249800_ref8","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2011.12344","article-title":"Trust but Verify: assigning prediction credibility by counterfactual constrained learning","author":"Chamon","year":"2020"},{"key":"2026021923163249800_ref9","article-title":"Underspecification presents challenges for credibility in modern machine learning","volume-title":"J Mach Learn Res","author":"D\u2019Amour","year":"2022"},{"key":"2026021923163249800_ref10","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1177\/0165551506070706","article-title":"The wisdom hierarchy: representations of the DIKW hierarchy","volume":"33","author":"Rowley","year":"2007","journal-title":"J Inf Sci"},{"key":"2026021923163249800_ref11","doi-asserted-by":"publisher","first-page":"e12974","DOI":"10.1111\/phc3.12974","article-title":"Reliability in machine learning","volume":"19","author":"Grote","year":"2024","journal-title":"Philos Compass"},{"key":"2026021923163249800_ref12","doi-asserted-by":"publisher","first-page":"103996","DOI":"10.1016\/j.jbi.2022.103996","article-title":"Evaluating pointwise reliability of machine learning prediction","volume":"127","author":"Nicora","year":"2022","journal-title":"J Biomed Inform"},{"key":"2026021923163249800_ref13","doi-asserted-by":"publisher","first-page":"103381","DOI":"10.1016\/j.rineng.2024.103381","article-title":"An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease","volume":"24","author":"Sathi","year":"2024","journal-title":"RINENG"},{"key":"2026021923163249800_ref14","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat Rev Phys"},{"key":"2026021923163249800_ref15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","article-title":"Explainable AI: a review of machine learning interpretability methods","volume":"23","author":"Linardatos","year":"2020","journal-title":"Entropy (Basel)"},{"key":"2026021923163249800_ref16","doi-asserted-by":"publisher","first-page":"e1312","DOI":"10.1002\/widm.1312","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger","year":"2019","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"2026021923163249800_ref17","doi-asserted-by":"publisher","first-page":"107727","DOI":"10.1016\/j.cmpb.2023.107727","article-title":"Credibility assessment of computational models according to ASME V&V40: application to the bologna biomechanical computed tomography solution","volume":"240","author":"Aldieri","year":"2023","journal-title":"Comput Methods Programs Biomed"},{"key":"2026021923163249800_ref18","doi-asserted-by":"publisher","volume-title":"Synthesis Lectures on Biomedical Engineering","year":"2024","DOI":"10.1007\/978-3-031-48284-7"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf100\/62365889\/bbaf100.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf100\/62365889\/bbaf100.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:16:39Z","timestamp":1771560999000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf100\/8068538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":18,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf100","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf100"}}