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大型语言模型在产生罕见疾病差异诊断中使用关键临床特征的准确性

Cathy Shyr¹, Rory J Tinker², Paul A Harris¹

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Studies in health technology and informatics

|August 8, 2025

概括

大型语言模型 (LLM) 可以帮助诊断罕见疾病. 聊天GPT-4o显示了最高的准确性,在与差异诊断的生物信息工具相结合时得到改善.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
罕见疾病的诊断罕见疾病的诊断

背景情况:

产生罕见疾病的差异诊断是具有挑战性和耗时的.
医生的专业知识严重影响诊断的准确性.
大型语言模型 (LLM) 在现实世界罕见疾病群体中的实用性尚未得到充分证实.

研究的目的:

在罕见疾病患者中评估LLMs (ChatGPT-4o,Llama 3.1-8B-Instruct) 和生物信息工具 (Exomiser) 的诊断准确性.
将这些工具的性能相互比较,并确定影响准确性的因素.

主要方法:

在424名罕见疾病患者中评估了ChatGPT-4o,Llama 3.1-8B-Instruct和Exomiser的诊断准确性.
利用了未诊断疾病网络的数据.
分析了患者因素,如症状发作的年龄,对诊断性能的影响.

主要成果:

聊天GPT-4o实现了最高的差异诊断准确率22.4%,超过了Exomiser (13.9%) 和Llama 3.1-8B-Instruct (11.6%).
症状发作的晚年与改善的ChatGPT-4o准确性相关.
结合ChatGPT-4o和Exomiser,与单个模型相比,准确度提高了30%.

结论:

LLM,特别是ChatGPT-4o,在协助罕见疾病差异诊断方面表现有前途.
将LLM与既有生物信息学工具相结合,可以提高诊断准确度.
对将人工智能和生物信息学整合到罕见疾病诊断中进行进一步的研究是有必要的.

关键词:

它们是Exomiser和Exomiser.罕见疾病是一种罕见的疾病.差异诊断是一种差异诊断.大型语言模型.

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