对眼科视觉和视觉语言基础模型的系统审查
Kai Jin1,2,3, Tao Yu1,2,3, Gui-Shuang Ying4
1Eye Center of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Advances in ophthalmology practice and research
|January 14, 2026
概括
视觉和视觉语言基础模型在眼科中显示出改善诊断和治疗的前景. 挑战包括数据质量,透明度和临床整合,需要进一步研究才能广泛采用.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 基础模型,包括大型语言模型和基于视觉的模型,在医学中提供了变革性的潜力.
- 在眼科中,这些AI框架可以提高诊断准确性,治疗规划和临床工作流程.
- 然而,与通用性和临床整合相关的挑战目前限制了它们的广泛部署.
研究的目的:
- 系统地审查目前关于视觉和视觉语言基础模型在眼科应用的证据.
- 确定关键应用,评估模型性能结果,并概述现有挑战.
- 要总结这些先进的AI模型在眼科护理中的潜力.
主要方法:
- 在PubMed,Web of Science,Scopus和Google Scholar进行了全面的文献搜索,搜索了2020年1月至2025年7月期间发表的研究.
- 包括的研究重点是应用于临床相关眼科应用的基础模型,例如视网膜疾病,青光眼和眼睛瘤.
- 结果措施包括模型性能指标,临床工作流集成和整体临床实用性.
主要成果:
- 十项研究符合纳入标准,证明了基础模型的高诊断准确性,其中一些在视网膜疾病诊断中达到高达95%的准确性.
- 模型的性能与经验丰富的临床医生在检测青光眼的进展和其他眼科疾病方面具有相似或优越的性能.
- 发现的关键局限性包括依赖大型数据集,计算需求,算法偏差,过度匹配和解释性问题阻碍临床医生的信任.
结论:
- 视觉和视觉语言基础模型对于眼科的进步具有显著的潜力,特别是在诊断视网膜疾病,玻璃眼和眼睛瘤方面.
- 解决与数据质量,透明度,伦理考虑和模型可解释性相关的挑战对于成功实施至关重要.
- 未来的研究应优先考虑改进模型性能,提高概括性,并为常规临床实践制定整合策略.
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