A Multiagent Summarization and Auto-Evaluation Framework for Medical Text: Development and Evaluation Study.

ABSTRACT:

Background

Although large language models (LLMs) show great promise in processing medical text, they are prone to generating incorrect information, commonly referred to as hallucinations. These inaccuracies present a significant risk for clinical applications where precision is critical. Additionally, relying on human experts to review LLM-generated content to ensure accuracy is costly and time-consuming, which sets a barrier against large-scale deployment of LLMs in health care settings.

Objective

The primary objective of this study was to develop an automatic artificial intelligence (AI) system capable of extracting structured information from unstructured medical data and using advanced reasoning techniques to support reliable clinical decision making. A key aspect of thi

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12707800 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature