评估大型语言模型作为选择第一个试验镜片参数的辅助工具,用于骨髓瘤学
Yijin Han1, Junhan Wei1, Jiaqi Wang1
1Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi'an, Shaanxi, China.
Frontiers in medicine
|February 18, 2026
概括
大型语言模型 (LLM) 在眼镜测量中显示出近视控制的前景. GPT-o3和GPT-4o在脊椎病理学配件中表现最好,尽管临床医生验证对于镜片参数至关重要.
科学领域:
- 视光学和视觉科学 视光学和视觉科学
- 医疗保健中的人工智能
- 眼科医生 眼科 眼科
背景情况:
- 脊椎外科治疗是近视控制的关键方法.
- 大型语言模型 (LLM) 正在成为临床环境中的潜在工具.
- 评估LLMs在专业的工作流程,如orthokeratology至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 的有效性,作为对近视控制的脊髓炎合过程中的辅助工具.
- 为了比较不同LLM在分析折射误差病例和推初始试验镜头参数方面的性能.
主要方法:
- 一个回顾性分析涉及四个LLM (GPT-4o,GPT-o3,GPT-4.1,克劳德3.7索内特).
- LLM分析了折射误差病例,以建议第一次试验的镜头参数.
- 进行了主观 (准确性,质量) 和客观 (参数差异) 的评估.
主要成果:
- 在LLM中,性能各不相同,GPT-o3和GPT-4o显示出优越的整体质量和准确性.
- 在第一次试验镜头参数中的可行性错误在校正回合中减少.
- LLM的输出显示出对特定参数的偏差,特别是基础曲线半径 (BC) 和后光区直径 (RZD) 的曲率半径.
结论:
- 实际上,LLM可能有助于在骨髓瘤学适配时的日常决策.
- 临床人员的监督和验证对于第一次试验镜片参数选择至关重要.
- 在LLM建议中的系统偏见,特别是对于BC和RZD,需要仔细考虑.
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