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

This study highlights the potential of LLMs as reliable tools for unstructured medical data summarization and evaluation to reduce the dependency on human experts and also states the limitations. The proposed framework, multiagent summarization and auto-evaluation, demonstrates scalability and adaptability for clinical applications while addressing key challenges like hallucination and position bias.

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Publication Details

Journal
JMIR AI
Published
2025-10-31
DOI
https://doi.org/10.2196/75932
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48

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article

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

Bo Wen, Farhana Zulkernine, Yuhao Chen
1 citations
JMIR AI
Topic Modeling
2.48
article

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

Bo Wen, Farhana Zulkernine, Yuhao Chen
article en
1 citations

Abstract

This study highlights the potential of LLMs as reliable tools for unstructured medical data summarization and evaluation to reduce the dependency on human experts and also states the limitations. The proposed framework, multiagent summarization and auto-evaluation, demonstrates scalability and adaptability for clinical applications while addressing key challenges like hallucination and position bias.

JMIR AIVol. 4
Queen's University (CA), Lustgarten Foundation (US)
Cleveland Clinic, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 8%
Topic Modeling
2.48
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