Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
This work addresses the need for reliable evaluation in high-stakes medical summarization, but it is incremental as it reviews existing literature without introducing new methods or data.
The paper reviews the current evaluation methods for large language models in clinical summarization tasks and proposes future directions to address the challenges of expert human evaluation due to resource constraints.
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.