使用多式大型语言模型 (ChatGPT) 进行角膜断层扫描的诊断解释
Jeremy C K Tan1, Minas T Coroneo1
1Department of Ophthalmology, Prince of Wales Hospital, Randwick, NSW, 2031, Australia.
American journal of ophthalmology case reports
|October 13, 2025
概括
一个大型语言模型 (LLM) 准确地从角膜断层扫描报告中提取关键指标,用于诊断眼睛疾病. 这项技术通过合成多式联运数据,显示出作为诊断辅助的前景.
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:
- 角膜断层扫描为诊断各种眼睛疾病提供了必不可少的数据.
- 解释复杂的诊断报告可能耗时,需要专门的专业知识.
研究的目的:
- 评估商业大型语言模型 (LLM) 在从原始角膜断层扫描 (Pentacam) 报告中提取和解释关键指标的有效性.
- 评估LLM在协助诊断角膜疾病方面的能力.
主要方法:
- 一个多式通用LLM分析了50只眼睛的匿名角膜断层扫描生物识别报告.
- 系统提示指导LLM提取角质量测量值,标和包度,并提供诊断.
主要成果:
- 该LLM在从角膜断层扫描报告中提取所有指定的指标时实现了100%的准确性.
- 在所有情况下,LLM的诊断与人类观察者一致.
- 精度测量和最大角质测量是诊断中最常引用的指标.
结论:
- 商用LLM可以准确地从角膜断层扫描报告中提取数据并制定诊断.
- 通过合成用于眼科应用的多式联络数据,LLM显示出其作为诊断辅助器的潜力.
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