{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:37:21Z","timestamp":1793353041599,"version":"4.5.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>Demographic bias in AI-driven clinical decision support systems (CDSS) represents one of the most consequential and least-addressed risks in healthcare AI. Large Language Models (LLMs) applied to emergency triage can silently perpetuate or amplify existing health disparities across thousands of patient decisions. This paper introduces FairGuard, a continuous bias detection and governance framework that embeds demographic fairness auditing directly into the LLM inference pipeline using four integrated mechanisms: (1) an equity-enforcing consent gate that applies demographic-blind access control; (2) a RAG corpus bias analyzer that traces class-directional misclassification to retrieval layer composition; (3) per-subgroup confusion matrix stratification computing \u0394F1 across demographic groups; and (4) a blockchain-anchored continuous monitoring layer that enforces a \u0394F1 \u2264 0.05 governance threshold across every inference batch. Evaluated on the MIMIC-IV Full Emergency dataset (N=300\u20131,000), FairGuard achieves a gender \u0394F1 of 0.020, well within threshold, while identifying RAG corpus composition as the root cause of conservative triage bias (50.9% Urgent \u2192\u00a0Non-Urgent misclassification), establishing that the observed bias is class-directional rather than demographically concentrated. FairGuard provides the first continuous, blockchain-enforced fairness governance mechanism for LLM-based emergency triage CDSS.<\/jats:p>","DOI":"10.3233\/shti260872","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:01:38Z","timestamp":1782986498000},"source":"Crossref","is-referenced-by-count":1,"title":["FairGuard: Blockchain-Enforced Continuous Fairness Governance for Demographically Equitable LLM-Based Emergency Triage Decision Support"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9722-5862","authenticated-orcid":false,"given":"Mutiullah","family":"Shaikh","sequence":"first","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-6205","authenticated-orcid":false,"given":"Ali","family":"Ebrahimi","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pardis","family":"Moradbeiki","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6898-4083","authenticated-orcid":false,"given":"Uffe Kock","family":"Wiil","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:01:39Z","timestamp":1782986499000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260872","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}