{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:35:55Z","timestamp":1789461355017,"version":"build-2803163510"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","award":["R01LM013362"],"award-info":[{"award-number":["R01LM013362"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Lancet Digital Health"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.landig.2026.101035","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T18:58:14Z","timestamp":1786474694000},"page":"101035","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Navigating fairness in artificial intelligence-based prediction models: theoretical constructs and practical applications"],"prefix":"10.1016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9343-0899","authenticated-orcid":false,"given":"Siri L","family":"van der Meijden","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3824-9349","authenticated-orcid":false,"given":"Madelena Y","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sesmu M","family":"Arbous","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart F","family":"Geerts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewout W","family":"Steyerberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6553-3455","authenticated-orcid":false,"given":"Tina","family":"Hernandez-Boussard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.landig.2026.101035_bib1","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.landig.2026.101035_bib2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.7326\/M23-2960","article-title":"The impact of health care algorithms on racial and ethnic disparities: a systematic review","volume":"177","author":"Siddique","year":"2024","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.landig.2026.101035_bib5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616865","article-title":"Fairness in machine learning: a survey","volume":"56","author":"Caton","year":"2024","journal-title":"ACM Comput Surv"},{"key":"10.1016\/j.landig.2026.101035_bib6","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2020.561802","article-title":"Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","volume":"3","author":"Fletcher","year":"2021","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.landig.2026.101035_bib7","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41746-023-00918-4","article-title":"A translational perspective towards clinical AI fairness","volume":"6","author":"Liu","year":"2023","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2026.101035_bib8","doi-asserted-by":"crossref","DOI":"10.1093\/jamiaopen\/ooae149","article-title":"Algorithmic individual fairness and healthcare: a scoping review","volume":"8","author":"Anderson","year":"2024","journal-title":"JAMIA Open"},{"key":"10.1016\/j.landig.2026.101035_bib9","first-page":"115","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput Surv"},{"key":"10.1016\/j.landig.2026.101035_bib10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3597199","article-title":"What-is and how-to for fairness in machine learning: a survey, reflection, and perspective","volume":"55","author":"Tang","year":"2023","journal-title":"ACM Comput Surv"},{"key":"10.1016\/j.landig.2026.101035_bib11","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.landig.2026.101035_bib12","doi-asserted-by":"crossref","unstructured":"Suresh H, Guttag JV. A framework for understanding sources of harm throughout the machine learning life cycle. EAAMO '21: Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization 1\u20139.","DOI":"10.1145\/3465416.3483305"},{"key":"10.1016\/j.landig.2026.101035_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102642","article-title":"Machine learning fairness notions: bridging the gap with real-world applications","volume":"58","author":"Makhlouf","year":"2021","journal-title":"Inf Process Manag"},{"key":"10.1016\/j.landig.2026.101035_bib14","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s43681-022-00195-z","article-title":"Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers","volume":"3","author":"Solanki","year":"2023","journal-title":"AI Ethics"},{"key":"10.1016\/j.landig.2026.101035_bib15","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1038\/s41467-022-32186-3","article-title":"Addressing fairness in artificial intelligence for medical imaging","volume":"13","author":"Ricci Lara","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.landig.2026.101035_bib16","article-title":"MEDFAIR: benchmarking fairness for medical imaging","author":"Zong","year":"2022","journal-title":"arXiv"},{"key":"10.1016\/j.landig.2026.101035_bib17","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","year":"2025","journal-title":"BMJ"},{"key":"10.1016\/j.landig.2026.101035_bib18","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1038\/s41746-025-01667-2","article-title":"A scoping review and evidence gap analysis of clinical AI fairness","volume":"8","author":"Liu","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2026.101035_bib19","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/s41746-024-01245-y","article-title":"Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor","volume":"7","author":"Ladin","year":"2024","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2026.101035_bib20","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1186\/s12916-025-04340-3","article-title":"Navigating fairness aspects of clinical prediction models","volume":"23","author":"Chakradeo","year":"2025","journal-title":"BMC Med"},{"key":"10.1016\/j.landig.2026.101035_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2022.104250","article-title":"Algorithmic fairness in computational medicine","volume":"84","author":"Xu","year":"2022","journal-title":"EBioMedicine"},{"key":"10.1016\/j.landig.2026.101035_bib22","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1093\/jamia\/ocae060","article-title":"Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models","volume":"31","author":"Chen","year":"2024","journal-title":"J Am Med Inform Assoc"},{"key":"10.1016\/j.landig.2026.101035_bib23","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1377\/hlthaff.2023.00545","article-title":"Promoting equity in clinical decision making: dismantling race-based medicine","volume":"42","author":"Hernandez-Boussard","year":"2023","journal-title":"Health Aff (Millwood)"},{"key":"10.1016\/j.landig.2026.101035_bib24","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adi8411","article-title":"Risk scores, label bias, and everything but the kitchen sink","volume":"10","author":"Zanger-Tishler","year":"2024","journal-title":"Sci Adv"},{"key":"10.1016\/j.landig.2026.101035_bib25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s11948-024-00507-y","article-title":"Reconstructing AI ethics principles: Rawlsian ethics of artificial intelligence","volume":"30","author":"Westerstrand","year":"2024","journal-title":"Sci Eng Ethics"},{"key":"10.1016\/j.landig.2026.101035_bib26","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s00146-022-01614-9","article-title":"\"Just\" accuracy? procedural fairness demands explainability in AI-based medical resource allocations","volume":"39","author":"Rueda","year":"2022","journal-title":"AI Soc"},{"key":"10.1016\/j.landig.2026.101035_bib27","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1177\/0037549720932656","article-title":"Computing fairness: ethics of modeling and simulation in public health","volume":"98","author":"Bak","year":"2022","journal-title":"Simulation"},{"key":"10.1016\/j.landig.2026.101035_bib28","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"Gianfrancesco","year":"2018","journal-title":"JAMA Intern Med"},{"key":"10.1016\/j.landig.2026.101035_bib29","article-title":"Survey on fairness notions and related tensions","volume":"11","author":"Alves","year":"2023","journal-title":"EURO J Decis Processes"},{"key":"10.1016\/j.landig.2026.101035_bib30","first-page":"1","article-title":"The measure and mismeasure of fairness","volume":"24","author":"Corbett-Davies","year":"2023","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.landig.2026.101035_bib31","series-title":"FAccT '23: proceedings of the 2023 ACM conference on fairness, accountability, and transparency","first-page":"688","article-title":"Ground(less)truth: a causal framework for proxy labels in human-algorithm decision-making","author":"Guerdan","year":"2023"},{"key":"10.1016\/j.landig.2026.101035_bib32","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1093\/eurheartj\/ehu207","article-title":"Towards better clinical prediction models: seven steps for development and an ABCD for validation","volume":"35","author":"Steyerberg","year":"2014","journal-title":"Eur Heart J"},{"key":"10.1016\/j.landig.2026.101035_bib33","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"10.1016\/j.landig.2026.101035_bib34","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s41746-020-0304-9","article-title":"Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities","volume":"3","author":"Paulus","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2026.101035_bib35","first-page":"348","article-title":"Identifying and predicting postoperative infections based on readily available electronic health record data","volume":"302","author":"van der Meijden","year":"2023","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.landig.2026.101035_bib36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"10.1016\/j.landig.2026.101035_bib37","article-title":"Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data","volume":"49","author":"van der Meijden","year":"2025","journal-title":"Lancet Reg Health Eur"},{"key":"10.1016\/j.landig.2026.101035_bib38","article-title":"Unveiling and mitigating bias in mental health analysis with large language models","author":"Wang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.landig.2026.101035_bib39","article-title":"FairEHR-CLP: towards fairness-aware clinical predictions with contrastive learning in multimodal electronic health records","author":"Wang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.landig.2026.101035_bib40","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1007\/s11606-013-2421-5","article-title":"Gender differences in risk of bloodstream and surgical site infections","volume":"28","author":"Cohen","year":"2013","journal-title":"J Gen Intern Med"},{"key":"10.1016\/j.landig.2026.101035_bib41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000386","article-title":"Fairness as an afterthought: an American perspective on fairness in model developer-clinician user collaborations","volume":"2","author":"Banja","year":"2023","journal-title":"PLoS Digit Health"},{"key":"10.1016\/j.landig.2026.101035_bib42","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0159224","article-title":"Racial-ethnic disparities in opioid prescriptions at emergency department visits for conditions commonly associated with prescription drug abuse","volume":"11","author":"Singhal","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.landig.2026.101035_bib43","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1097\/ADM.0000000000001104","article-title":"Improving research on racial disparities in access to medications to treat opioid use disorders","volume":"17","author":"Mark","year":"2023","journal-title":"J Addict Med"},{"key":"10.1016\/j.landig.2026.101035_bib44","doi-asserted-by":"crossref","first-page":"26344","DOI":"10.1609\/aaai.v39i25.34833","article-title":"Fairness-accuracy trade-offs: A causal perspective","volume":"39","author":"Plecko","year":"2025","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.landig.2026.101035_bib45","first-page":"1","article-title":"The unfairness of fair machine learning: leveling down and strict egalitarianism by default","volume":"30","author":"Mittelstadt","year":"2023","journal-title":"Mich Tech L Rev"},{"key":"10.1016\/j.landig.2026.101035_bib46","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1038\/s42256-023-00697-3","article-title":"Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning","volume":"5","author":"Yang","year":"2023","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.landig.2026.101035_bib47","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s43856-025-00965-w","article-title":"Diagnostic framework to validate clinical machine learning models locally on temporally stamped data","volume":"5","author":"Schuessler","year":"2025","journal-title":"Commun Med (Lond)"},{"key":"10.1016\/j.landig.2026.101035_bib48","series-title":"FAccT '21: proceedings of the 2021 ACM conference on fairness, accountability, and transparency","first-page":"375","article-title":"Measurement and fairness","author":"Jacobs","year":"2021"},{"key":"10.1016\/j.landig.2026.101035_bib49","first-page":"1","article-title":"The measure and mismeasure of fairness","volume":"24","author":"Corbett-Davies","year":"2023","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.landig.2026.101035_bib50","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/s41746-025-01482-9","article-title":"Could transparent model cards with layered accessible information drive trust and safety in health AI?","volume":"8","author":"Gilbert","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2026.101035_bib51","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1038\/s41591-025-03680-0","article-title":"Promoting transparency in AI for biomedical and behavioral research","volume":"31","author":"Hernandez-Boussard","year":"2025","journal-title":"Nat Med"},{"key":"10.1016\/j.landig.2026.101035_bib52","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1038\/s41591-022-01993-y","article-title":"The AI life cycle: a holistic approach to creating ethical AI for health decisions","volume":"28","author":"Ng","year":"2022","journal-title":"Nat Med"},{"key":"10.1016\/j.landig.2026.101035_bib53","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41746-025-01786-w","article-title":"Framework for bias evaluation in large language models in healthcare settings","volume":"8","author":"Templin","year":"2025","journal-title":"NPJ Digit Med"}],"container-title":["The Lancet Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589750026000580?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589750026000580?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T20:55:41Z","timestamp":1786568141000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2589750026000580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":51,"alternative-id":["S2589750026000580"],"URL":"https:\/\/doi.org\/10.1016\/j.landig.2026.101035","relation":{},"ISSN":["2589-7500"],"issn-type":[{"value":"2589-7500","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Navigating fairness in artificial intelligence-based prediction models: theoretical constructs and practical applications","name":"articletitle","label":"Article Title"},{"value":"The Lancet Digital Health","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.landig.2026.101035","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"101035"}}