A small, domain-specialized retrieval-augmented LLM can safely and substantially outperform far larger generalist models on a sensitive pediatric clinical domain, demonstrating that specialization plus citation-grounded retrieval, not scale alone, is a practical path to deployable clinical AI.
Background/Objectives: Large language models (LLMs) show promise for clinical decision support, yet their accuracy in interpreting specialized medical guidelines remains uncertain. Retrieval-augmented generation (RAG) may enhance performance by grounding responses in authoritative knowledge bases. This study aimed to c...
Andreas Vollmer, Lara K. Schorn, Felix Schrader et al.· Diagnostics· 0 citations
NCCN-anchored RAG outperformed both baseline GPT-5 and a literature-anchored clinical AI without direct guideline access, and external validation of guideline anchoring's operational importance provides external validation of guideline anchoring's operational importance.
D. Dukes, C. Yost, Runzhi Wang et al.· Gynecologic Oncology· 0 citations
VITA's advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower, and this results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves gro...
P. Reddy, C. Mandke, Suvrankar Datta et al.· 0 citations
LLMs can support communication and education following spine surgery when used with structured prompting when used with structured prompting and ChatGPT and Claude showed the highest correctness and completeness, particularly for practitioner-directed answers.
S. Wegmann, T. Rosenkranz, Philipp Egenolf et al.· European spine journal· 0 citations
RAG-based LLM systems improved performance on anesthesiology board-style questions, but gains depended strongly on retrieval design, and reasoning-oriented models demonstrated that multistep reasoning can, in some settings, compensate for larger parameter scale.
It is suggested that RAG LLMs may be supplementary tools for evidence retrieval and synthesis but cannot, at this time, fully replace comprehensive expert review of the medical literature.
P. A. Krump, Mallory N. Blasingame, T. Koonce et al.· medRxiv· 0 citations
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