GuideSkill boosts clinical LLM accuracy by 18.49% without model updates

A new method called GuideSkill improves the macro-average accuracy of large language models (LLMs) in clinical diagnosis by 13.45% on average without updating the model, and by 18.49% when refined with case data, according to a paper on arXiv. GuideSkill-Evo raises gold-label diagnostic skill coverage from 56.5% to 99.5% and outperforms direct LLM inference by 18.49% relative, enabling more accurate clinical diagnosis without expensive retraining and making reasoning paths easier to audit.

arXiv https://arxiv.org/abs/2607.26160 GuideSkill boosts clinical LLM accuracy by 18.49% without model updates Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. GuideSkill improves macro-average accuracy of LLMs in clinical diagnosis by 13.45% on average without updating the model, and by 18.49% when refined with case data. This enables shipping more accurate clinical diagnosis models without requiring expensive LLM retraining or fine-tuning, directly impacting production deployments that rely on guideline-grounded reasoning. GuideSkill-Evo raises gold-label diagnostic skill coverage from 56.5% to 99.5% and beats direct LLM inference by 18.49% relative without updating the backbone. For production clinical agents, the useful shift is from retrieving guideline text to executing guideline-derived rules as an external, inspectable scoring layer, which can improve accuracy and coverage while avoiding model fine-tuning and making reasoning paths easier to audit.