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An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented

Fidelis Fidelis Alu¹, Sunkanmi Oluwadare¹

¹School of Information Technology, University of Cincinnati, Cincinnati, OH, United States.

Frontiers in Artificial Intelligence

|February 20, 2026

Summary

This study proposes a framework for auditable artificial intelligence (AI) clinical decision support systems. It enhances transparency and trust by linking AI recommendations to evidence-based sources and logging system processes.

Keywords:

artificial intelligence (AI)auditability clinical decision support (CDS)data provenance explainability healthcare informatics source verification trustworthy AI

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Trustworthy AI

Background:

Clinical decision support (CDS) systems using artificial intelligence (AI) show potential but face limited adoption due to transparency and accountability concerns.
Generative and data-driven AI often lack clear explanations for their recommendations, hindering clinical trust and verification.
Existing AI in healthcare struggles with demonstrating the evidentiary basis for its outputs.

Purpose of the Study:

To present a conceptual framework for auditable and source-verified AI-based clinical decision support.
To enhance transparency, verifiability, and accountability in AI-driven healthcare recommendations.
To improve clinician trust and regulatory readiness for AI in clinical settings.

Main Methods:

Integration of a curated medical knowledge base with provenance metadata.
Utilizing a retrieval-augmented reasoning (RAG) engine to link recommendations to guidelines and peer-reviewed sources.
Implementation of a tamper-evident audit logging mechanism for recording system processes and evidence.

Main Results:

The article presents a synthesized system design, not a new algorithm or prototype.
The proposed framework aims to improve traceability of AI recommendations by linking them to verifiable sources.
It addresses key challenges including knowledge governance, citation fidelity, bias, usability, privacy, and regulatory alignment.

Conclusions:

The conceptual framework provides a pathway for developing trustworthy, auditable AI clinical decision support.
Addressing feasibility challenges is crucial for successful implementation and validation.
A staged evaluation roadmap involving simulations and user research is proposed for future empirical validation.