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<p>Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated the current landscape of evaluation metrics for NLG in healthcare and proposed a future direction to address the resource constraints of expert human evaluation while balancing alignment with human judgments.</p>

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Current and future state of evaluation of large language models for medical summarization tasks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月3日

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  • Emma Croxford,
  • Yanjun Gao,
  • Nicholas Pellegrino,
  • Karen Wong,
  • Graham Wills,
  • Elliot First,
  • Frank Liao,
  • Cherodeep Goswami,
  • Brian Patterson,
  • Majid Afshar

摘要

Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated the current landscape of evaluation metrics for NLG in healthcare and proposed a future direction to address the resource constraints of expert human evaluation while balancing alignment with human judgments.