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在激光视力校正中应用多式大型语言模型用于安全指标计算和禁忌预测

Joon Yul Choi¹, Doo Eun Kim², Sung Jin Kim²

¹Department of Biomedical Engineering, Yonsei University, Wonju, South Korea.

NPJ digital medicine

|February 3, 2025

概括

像ChatGPT-4这样的大型语言模型在激光视觉校正安全方面显示出希望. 他们可以分析眼部数据,预测禁忌,并改善决策支持,以获得更好的患者结果.

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:

评估安全指标和禁忌对于激光视力校正至关重要.
传统的方法难以处理复杂的,非结构化的眼球数据.

研究的目的:

评估多模式大语言模型 (LLM) 在激光视力校正安全分析中的潜力.
将LLM的表现与传统的机器学习模型进行比较.

主要方法:

利用ChatGPT-4,一个多式联络LLM,分析眼部数据,包括角膜拓.
使用LLM开发安全指标的计算器代码.
与传统的机器学习方法比较LLM性能.

主要成果:

聊天GPT-4精确分析了眼睛数据,并计算了关键安全指标.
与传统模型相比,LLM在处理非结构化数据和角膜拓学方面表现出卓越的性能.
该LLM的模式独立系统促进了高效和准确的数据分析.

结论:

多模式LLM,如ChatGPT-4,作为眼科决策支持工具具有重大潜力.
激光视力校正仪在提高激光视力校正程序的安全性和准确性方面取得了进展.
对处理时间和集成的进一步研究是有必要的.

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