{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T02:29:18Z","timestamp":1786069758525,"version":"3.56.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Artificial intelligence (AI) has shown promise in supporting clinical decision making, yet adoption in healthcare remains limited by concerns regarding transparency, verifiability, and accountability of AI-generated recommendations. In particular, generative and data-driven CDS systems often provide outputs without clearly exposing the evidentiary basis or reasoning process underlying their conclusions. This article presents a conceptual framework for auditable and source-verified AI-based clinical decision support, grounded in principles from evidence-based medicine, data provenance, and trustworthy AI. The proposed architecture integrates a curated medical knowledge base with explicit provenance metadata, a retrieval-augmented reasoning (RAG) engine that links generated recommendations to identifiable clinical guidelines and peer-reviewed sources, and a tamper-evident audit logging mechanism that records system inputs, retrieved evidence, and inference steps for retrospective review. This work does not introduce a new algorithm nor report a prototype implementation; rather, it synthesizes existing technical approaches into a coherent system design intended to improve traceability, clinician trust, and regulatory readiness. Key feasibility challenges are discussed, including knowledge-base governance and updating, citation fidelity in RAG architectures, bias propagation from underlying evidence, latency and usability trade-offs, privacy considerations, and alignment with emerging regulatory frameworks such as FDA Software as a Medical Device guidance and the European Union Artificial Intelligence Act. The article concludes by outlining a staged evaluation roadmap involving simulation studies and clinician-centered user research to guide future implementation and empirical validation.<\/jats:p>","DOI":"10.3389\/frai.2026.1737532","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T06:48:49Z","timestamp":1770187729000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance"],"prefix":"10.3389","volume":"9","author":[{"given":"Fidelis Fidelis","family":"Alu","sequence":"first","affiliation":[{"name":"School of Information Technology, University of Cincinnati","place":["Cincinnati, OH, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunkanmi","family":"Oluwadare","sequence":"additional","affiliation":[{"name":"School of Information Technology, University of Cincinnati","place":["Cincinnati, OH, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"6495","DOI":"10.3390\/s23146495","article-title":"Data provenance in healthcare: approaches, challenges, and future directions","volume":"23","author":"Ahmed","year":"2023","journal-title":"Sensors (Basel, Switzerland)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e0000016","DOI":"10.1371\/journal.pdig.0000016","article-title":"To explain or not to explain?\u2014artificial intelligence explainability in clinical decision support systems","volume":"1","author":"Amann","year":"2022","journal-title":"PLOS Digit Health"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"1\n          15\n          \n            \n              Androulaki\n              E.\n            \n            \n              Barger\n              A.\n            \n            \n              Bortnikov\n              V.\n            \n            \n              Cachin\n              C.\n            \n            \n              Christidis\n              K.\n            \n            \n              De Caro\n              A.\n            \n          \n          10.1145\/3190508.3190538\n          Hyperledger Fabric. 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