JMIR Publications

Background:

Large language models (LLMs) hold substantial promise for clinical tasks including documentation, differential diagnosis support, and evidence synthesis. However, their tendency to generate hallucinated (factually incorrect) outputs and to propagate demographic biases poses critical patient safety risks. Retrieval-augmented generation (RAG) has emerged as a technical approach to mitigate these limitations by grounding model outputs in external, verifiable knowledge sources.

Objective:

This viewpoint critically evaluates RAG as a strategy for improving factual accuracy and reducing bias in clinical AI systems, and examines its technical foundations, documented early applications, regulatory landscape, and policy implications for responsible deployment in healthcare.

Methods:

We conducted a targeted, non-systematic review of recent peer-reviewed publications, preprints, and regulatory guidance documents pertaining to RAG, large language models, and AI governance in clinical settings. Literature was identified through domain expertise and citation tracing rather than a formal systematic search strategy. Studies discussed are illustrative rather than exhaustive.

Results:

Proof-of-concept evaluations demonstrate that RAG substantially outperforms standalone LLMs in specific clinical tasks. In perioperative clinical guidance, a RAG-augmented GPT-4.0 system achieved 91.4% accuracy across 14 clinical scenarios. ChatRWD, a RAG-based real-world evidence system, produced clinically appropriate responses for 29 of 50 queries (58%), compared with 2–10% for general-purpose LLMs. RAG also attenuates bias by restricting retrieval to curated evidence sources, though representational gaps in retrieval corpora can persist. Regulatory frameworks, including FDA lifecycle guidance, EU AI Act provisions, and joint international guiding principles are converging on continuous oversight for AI-enabled clinical systems.

Conclusions:

RAG offers a compelling framework for improving the transparency and factual grounding of clinical AI. However, evidence from the studies reviewed here is largely limited to proof-of-concept evaluations with narrow scope, and broad clinical effectiveness has not yet been established. Responsible deployment requires equitable corpus design, standardized evaluation metrics, human-in-the-loop oversight, and regulatory frameworks adapted for dynamic, retrieval-augmented systems.