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基于眼睛高血压治疗研究数据集诊断青光眼,使用聊天生成预训练变压器作为大型语言模型

Hina Raja^1,2, Xiaoqin Huang¹, Mohammad Delsoz¹

¹Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, Tennessee.

Ophthalmology science

|September 30, 2024

概括

这项研究评估了使用眼睛高血压治疗研究数据集进行青光眼诊断的大型语言模型. 与ChatGPT 3.5相比,ChatGPT 4.0的准确性更高,显示出临床应用的前景.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

青光眼的诊断依赖于全面的患者数据.
大型语言模型 (LLM) 为分析复杂的医疗数据集提供了潜力.
评估LLM诊断能力对于其临床整合至关重要.

研究的目的:

评估聊天生成预训练变压器 (ChatGPT) 在识别青光眼的诊断性能.
使用眼睛高血压治疗研究 (OHTS) 数据集,比较ChatGPT 3.5和ChatGPT 4.0的诊断准确度.
探索LLMs作为一种用于格洛科马诊断的工具的实用性.

主要方法:

利用眼睛高血压治疗研究 (OHTS) 数据集,包括来自1585名受试者的3170只眼睛.
将表格患者数据 (人口统计,临床,眼睛,视野,视神经头部照片,病史) 转换为文本病例报告.
采用ChatGPT API来查询基于病例报告的3.5和4.0版本的青光眼诊断.

主要成果:

聊天GPT 3.5 实现了接收器操作特征曲线 (AUC) 下的面积为 0.74,准确度为 66%,特异性为 64%,灵敏度为 85%,F1 评分为 0.72.
聊天GPT 4.0的AUC达到0.76,准确度为87%,特异性为90%,灵敏度为61%,F1得分为0.92.
与ChatGPT 3.5.5相比,ChatGPT 4.0的整体准确性和特异性都更高.

结论:

聊天GPT 4.0显示了从结构化数据中对玻璃眼病诊断的有希望的准确性,超过了聊天GPT 3.5.5.
聊天GPT 3.5表现出更高的灵敏度,而聊天GPT 4.0在特异性方面表现出色.
像ChatGPT这样的LLM可以成为分析眼睛高血压患者数据的宝贵工具,未来的多模式版本可能会进一步提高诊断准确性.

关键词:

聊天GPT 聊天GPT 聊天诊断诊断诊断诊断诊断眼光障碍眼光障碍眼光障碍眼光障碍大型语言模型.

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