mlsanityData to 7 Oct 2026

GuidelineGuard: An Agentic Framework for Medical Note Evaluation with Guideline Adherence

arXiv 2411.06264 ↗First posted 9 Nov 2024cs.AI cs.IRcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Although rapid advancements in Large Language Models (LLMs) are facilitating the integration of artificial intelligence-based applications and services in healthcare, limited research has focused on the systematic evaluation of medical notes for guideline adherence. This paper introduces GuidelineGuard, an agentic framework powered by LLMs that autonomously analyzes medical notes, such as hospital discharge and office visit notes, to ensure compliance with established healthcare guidelines. By identifying deviations from recommended practices and providing evidence-based suggestions, GuidelineGuard helps clinicians adhere to the latest standards from organizations like the WHO and CDC. This framework offers a novel approach to improving documentation quality and reducing clinical errors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 29 references that are in the map. First posted on arXiv 9 Nov 2024.

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16 of 29 references.

References · 16

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4 more
  1. 2024Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultationcited by 17
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