AUTHOR=Alu Fidelis Fidelis , Oluwadare Sunkanmi 
  
TITLE=An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance
  
JOURNAL=Frontiers in Artificial Intelligence
  
VOLUME=Volume 9 - 2026
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1737532
  
DOI=10.3389/frai.2026.1737532
  
ISSN=2624-8212
  
ABSTRACT=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.