Authentic Sources, Potentially Unsafe Conclusions: Evidence Traceability Does Not Ensure Clinical Reliability in Retrieval-Augmented Generation for Perioperative IBD

Retrieval-augmented generation (RAG) is increasingly used to reduce hallucination in medical large language models (LLMs), but authentic retrieval does not necessarily ensure clinically correct output. We evaluated whether full-text-enriched RAG improves the clinical reliability of locally deployed models for perioperative inflammatory bowel disease (IBD) question answering.

We constructed a locally deployed full-text IBD perioperative knowledge base and a 50-question high-difficulty benchmark. Two 8B models (Qwen3-VL-8B and DeepSeek-R1-Distill-Llama-8B) were evaluated under matched no-retrieval and RAG conditions. Three physicians independently scored clinical accuracy, patient safety, and faithfulness under a source-blinded, format-homogeneous protocol. Evidence traceability was assessed objectively by verifying all retrieved PubMed identifiers (PMIDs) against PubMed.

All retrieved PMIDs existed in PubMed (236 verifications; 187 unique), confirming source-existence authenticity at the retrieval layer. RAG significantly improved Qwen3 accuracy (Δ=+0.44, P<0.001) but not DeepSeek accuracy (Δ=+0.21, P=0.09), and the model-by-retrieval interaction for accuracy was not significant. Patient safety showed no reliable benefit. Faithfulness decreased significantly with RAG on Qwen3 (Δ=-0.24, P<0.001), with a significant model-by-retrieval interaction. Inter-rater reliability was excellent (ICC 0.86-0.93).

PubMed-based retrieval achieved source-existence authenticity but did not consistently improve clinical correctness or safety. Authentic references could accompany incorrect or unsafe conclusions, indicating that evidence traceability and clinical reliability should be evaluated separately. Additional model-external safety mechanisms may be warranted in high-risk settings but require independent validation.