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Evaluating Large Language Model–Generated Clinical Summaries Through a Dual-Perspective Framework
Brian Han;
Traci Barnes;
Charitha D Reddy;
Andrew Y Shin
ABSTRACT
Large language models (LLMs) are increasingly used by patients and families to interpret complex medical documentation, yet most evaluations focus only on clinician-judged accuracy. In this study, 50 pediatric cardiac ICU notes were summarized using ChatGPT-4o and reviewed by both physicians and parents, who rated readability, accuracy, and helpfulness. There were important discrepancies between parents and clinicians in the realm of helpfulness with important insight on summaries regarding clinical accuracy and readability, highlighting the need for dual-perspective frameworks that balance clinical precision with patient understanding
Citation
Please cite as:
Han B, Barnes T, Reddy CD, Shin AY
Evaluating Large Language Model–Generated Clinical Summaries Through a Dual-Perspective Framework: Retrospective Observational Study