评估大模型在医疗摘要任务中的表现，提出改进评价方法的建议。
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
- 综述现有医疗摘要评估方法与挑战
- 指出专家人工评估资源不足的问题
- 适合关注医疗AI评估的研究者参考
大型语言模型在临床自然语言生成方面取得进展，为处理大量医学文本提供了可能。然而，医学领域高风险特性要求评估具备可靠性，而当前仍面临挑战。本文通过叙事性综述，评估临床摘要任务的现有评价状况，并提出未来方向以应对专家人工评估资源有限的问题。
原文摘要 · Abstract (English)
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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