在眼科临床实践中评估大型语言模型的能力:多场景定量研究
Mu-Yang Wei1, Yu-Lin Li1, Shu-Yan Liu1
1Department of Ophthalmology, The Second Norman Bethune Hospital of Jilin University, Changchun, China.
Frontiers in cell and developmental biology
|December 18, 2025
概括
这项研究比较了眼科中的五种大型语言模型 (LLM). 双子1.5闪在临床辅助和患者沟通方面表现出色,而Claude 3 Opus在诊断推理和文本可读性方面领先.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 对大型语言模型 (LLM) 的比较评估对于眼科等专业领域至关重要.
- 五个著名的LLM (ChatGPT 4,Claude 3 Opus,Gemini 1.5 Flash,ERNIE 3.5,iFLY Healthcare) 进行了系统的评估.
- 该研究旨在量化眼科医学的关键临床领域的LLM绩效.
研究的目的:
- 为在眼科实践中整合LLM提供基于证据的指导.
- 在模拟的眼科场景中评估领先的LLM的表现.
- 建立优化眼科人工智能模型开发的实际基础.
主要方法:
- 在三个模拟的眼科场景中评估了LLM:临床协助,诊断/治疗和医生与患者的沟通.
- 绩效指标包括准确性,完整性,可读性,临床推理和情绪/社会参与.
- 具体任务包括回答50个临床问题,375个考试问题和20个基于SPIKES的沟通场景.
主要成果:
- 双子 1.5 闪光显示在临床协助和强大的患者沟通中卓越的准确性和完整性.
- 克劳德3 Opus在诊断和治疗方面取得了最高的准确性 (81.07%) 并产生了最易读的文本.
- 所有评估的LLM都在资格考试中超过了临床推理的合格门.
结论:
- 双子座1.5闪光灯在生成准确的临床内容和患者参与方面表现出色.
- 克劳德3作品展示了卓越的临床推理和文本可读性.
- 结果验证了LLM的临床潜力,并为眼科AI应用提供了选择标准.
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