Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review

TL;DR

This paper reviews how large language models are evaluated for medical text summarization, highlighting current challenges and proposing future directions to improve assessment methods in high-stakes clinical applications.

Contribution

It provides a comprehensive overview of evaluation methods for clinical summarization by large language models and suggests future research directions to overcome resource limitations.

Findings

Current evaluation methods are resource-intensive and limited in scope.

There is a need for standardized, scalable evaluation frameworks.

Future directions include developing automated and semi-automated evaluation techniques.

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.