When Guidelines Lag Evidence: ESM-Enhanced RAG for Bias-Resistant Clinical AI

As large language models (LLMs) integrate into clinical practice, guideline development, and patient decision support, their tendency to hallucinate (generating fluent yet factually inaccurate outputs) presents a serious risk to evidence-based care. Retrieval-Augmented Generation (RAG) offers a meaningful improvement by linking responses to external knowledge sources, typically reducing error rates by ~30–50% in controlled evaluations. RAG establishes a new standard for AI precision, enabling models like GPT to effectively navigate the nuances of advanced clinical decision-making. However, RAG alone is insufficient when retrieval yields selectively curated or biased references, which can reinforce inherited institutional assumptions rather than prioritizing unfiltered, high-veracity evidence essential for AI-enhanced applications. This article introduces ESM‑Enhanced RAG as a general‑purpose veracity framework for AI systems, using medicine as a proof‑of‑concept domain to both validate the approach and educate clinicians about bias‑reduced AI.