在发病前预测眼光障碍使用大型语言模型聊天机器人
Xiaoqin Huang1, Hina Raja1, Yeganeh Madadi1
1Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center (X.H., H.R., Y.M., M.D., S.Y.), Memphis, Tennessee.
American journal of ophthalmology
|June 1, 2024
概括
聊天GPT4.0显示在预测眼睛高血压前一年格洛科马转换的合理能力. 这种大型语言模型显示了改善眼研究和临床护理的潜力.
科学领域:
- 眼科和人工智能的人工智能
- 医疗信息学 医疗信息学
- 玻璃眼研究研究 玻璃眼研究
背景情况:
- 眼睛高血压 (OHT) 是青光眼的前体,需要准确的预测方法.
- 早期发现绿眼转化对于及时干预和视力保护至关重要.
研究的目的:
- 评估ChatGPT,一个大型语言模型 (LLM) 在预测眼睛高血压转化为青光眼的有效性.
- 用眼睛高血压治疗研究 (OHTS) 的数据来评估ChatGPT 4.0和3.5在预测青光眼发展方面的表现.
主要方法:
- 一个回顾性病例对照研究,使用1504名OHTS参与者的3008只眼睛.
- 人口,临床,眼睛,视神经头部和视野参数从格洛科马发病前一年被提取.
- 将表格数据转换为文本格式,以便通过其API提示ChatGPT预测青光眼转换.
主要成果:
- 在预测青光眼转化时,ChatGPT 4.0实现了75%的准确性,AUC为0.67,敏感度为56%,特异性为78%,加权F1得分为0.77.
- 聊天GPT 3.5的性能较低,准确度为61%,AUC为0.62,灵敏度为64%,特异性为59%,加权F1得分为0.63.
结论:
- 聊天GPT 4.0在预测大眼发病前一年的发展方面表现合理,比聊天GPT 3.5.5的表现更好.
- 临床医学士课程显著有望提高青光眼的研究和临床实践.
- 未来开发眼科专用LLM,整合多式联络数据和主动学习,可以改善临床整合.
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