Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
Emma Croxford
Yanjun Gao
Nicholas Pellegrino
Karen K. Wong
Graham Wills
Elliot First
Frank J. Liao
Cherodeep Goswami
Brian Patterson
Majid Afshar

Abstract
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In this narrative review, we assess the current evaluation state for clinical summarization tasks and propose future directions to address the resource constraints of expert human evaluation.
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