EndoGPT: A Proof-of-concept Large Language Model Based Assistant for the Management of Thyroid Nodules

Meghal Shah, Eric J. Kuo, Jennifer H. Kuo, Shawn Hsu, Catherine McManus, Rachel Liou, James A. Lee, Tejas S. Sathe
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Abstract

Large language models (LLMs) are increasingly being explored for their potential to simulate clinical reasoning. Here, we demonstrate our initial experience using the GPT-4o LLM along with prompt engineering and knowledge retrieval to develop EndoGPT, a clinical decision support tool for the management of thyroid nodules. In a pilot study of 50 cases, EndoGPT demonstrated an 83% concordance rate with expert surgeons’ assessments and plans. The highest concordance was in diagnosis (93%), followed by the need for an operation (82%) and type of operation (69%). This work suggests that LLM-based assistants may play a useful role in assisting clinicians in the future.
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EndoGPT:基于大语言模型的甲状腺结节管理助手的概念验证
大型语言模型(LLM)在模拟临床推理方面的潜力正日益受到人们的关注。在这里,我们展示了使用 GPT-4o LLM 以及提示工程和知识检索来开发 EndoGPT 的初步经验,这是一种用于甲状腺结节管理的临床决策支持工具。在对 50 个病例的试点研究中,EndoGPT 与专家外科医生的评估和计划的吻合率高达 83%。一致性最高的是诊断(93%),其次是手术需求(82%)和手术类型(69%)。这项工作表明,基于 LLM 的助手在未来协助临床医生方面可能会发挥有益的作用。
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