EndoGPT: A Proof-of-concept Large Language Model Based Assistant for the Management of Thyroid Nodules
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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.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。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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