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Updated: May 28, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

在视觉现场测试评估中使用ChatGPT-4.

Gulsah Gumus Akgun¹, Cigdem Altan¹, Ali Safa Balci¹

¹Beyoglu Eye Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.

Clinical & experimental optometry

|February 12, 2025

概括

聊天GPT-4在解释视觉场测试名称和模式方面表现出很高的准确性,但在复杂的地图解释和诊断方面存在困难. 这突显了人工智能在眼科中的潜力,在详细的视野分析中还有改进的余地.

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:

视觉现场测试对于诊断和管理眼科疾病,如绿眼病至关重要.
集成先进的人工智能,如ChatGPT-4,有可能提高临床决策和眼科护理的可访问性.

研究的目的:

评估ChatGPT-4在解释视觉现场测试的各种组件中的准确性和能力.
将AI解释与专家眼科医生的评估进行比较.

主要方法:

通过ChatGPT-4分析了30名患者的视野打印图.
在识别测试元素 (名称,模式,可靠性,地图) 和诊断分类方面评估了性能.
对比了两位青光眼顾问的解释.

主要成果:

对于测试名称 (100%),模式 (90%) 和可靠性指数 (93.3%),ChatGPT-4实现了高准确度.
解释偏差和灰度地图的准确性较低 (分别为66.7%和30%).
准确的"正常"分类或诊断建议仅在33.3%的病例中实现.

结论:

像ChatGPT-4这样的大型语言模型在分析视觉现场测试,特别是数值数据方面显示出巨大的潜力.

关键词:

人工智能的人工智能是人工智能.聊天GPT 聊天GPT 的意思大型语言模型视觉现场测试视觉现场测试

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目前在解释复杂的视野图和提供准确的诊断建议方面存在局限性.

人工智能需要进一步开发,以充分支持视野缺陷的临床解释.