临床辅助的眼科基础模型:随机对照试验
Yilan Wu1,2, Bo Qian3,4,5, Tingyao Li3,4
1Beijing Visual Science and Translational Eye Research Institute (BERI), Beijing Tsinghua Changgung Hospital Eye Center, Tsinghua Medicine, Tsinghua University, Beijing, China.
Nature medicine
|August 28, 2025
概括
EyeFM是一款人工智能眼部护理副驾驶仪, 显著提高了眼科医生在视网膜疾病查中的诊断准确度和转诊率. 这种人工智能工具可以提高患者的治疗效果,
科学领域:
- 眼科 眼科
- 人工智能
- 医学成像
背景情况:
- 在医疗保健中对人工智能 (AI) 进行临床评估的需求日益增加.
- 开发用于临床应用的多式视觉语言人工智能模型.
研究的目的:
- 开发和评估EyeFM,一个多式视觉语言人工智能副驾驶器用于眼睛护理.
- 评估EyeFM在改善眼科医生的诊断性能和患者结果方面的有效性.
主要方法:
- 多方面评估包括追溯验证,多国疗效验证和双面随机对照试验 (RCT).
- EyeFM在五种模式和全球临床文本中预先训练了1450万张眼镜图像.
- 在中国,RCT涉及668名参与者和16名眼科医生,将EyeFM辅助使用与标准护理进行了比较.
主要成果:
- 使用EyeFM的眼科医生取得了显著更高的正确诊断率 (92. 2%与75. 4%) 和转诊率 (92. 2%与80. 5%).
- 观察到 EyeFM 的临床报告标准化得分有所改善 (中位数为33对37).
- 干预组在随访时表现出更高的自我管理 (70. 1% vs 49. 1%) 和转诊建议 (33. 7% vs 20. 2%).
结论:
- 在眼科中,EyeFM作为临床辅助飞行员具有显著的实用性.
- 实施EyeFM可以提高眼科医生的表现,并改善患者在视网膜疾病查中的结果.
- 发现了强烈的用户接受度和对临床决策和患者坚持治疗的积极影响.
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