山东大学耳鼻喉眼学报 ›› 2026, Vol. 40 ›› Issue (3): 115-120.doi: 10.6040/j.issn.1673-3770.0.2025.334

• 综述 • 上一篇    

人工智能在干眼管理中的应用进展

杨冠英1,李元彬2   

  1. 1. 山东第二医科大学 临床医学院, 山东 潍坊 261053;
    2. 烟台毓璜顶医院 眼科, 山东 烟台 264000
  • 发布日期:2026-05-22
  • 通讯作者: 李元彬. E-mail:690539122@qq.com
  • 基金资助:
    烟台市科技计划项目(2024JCYJ05)

Advances in the application of artificial intelligence to dry eye disease management

YANG Guanying1, LI Yuanbin2   

  1. 1. School of Clinical Medicine, Shandong Second Medical University, Weifang 261053, Shandong, China2. Department of Ophthalmology, Yantai Yuhuangding Hospital, Yantai 264000, Shandong, China
  • Published:2026-05-22

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摘要： 人工智能(artificial intelligence, AI)是通过模拟人类思维与学习过程,实现数据分析与辅助决策的技术体系。干眼(dry eye disease, DED)是一种常见的慢性眼表疾病,传统诊疗过程依赖人工判断,存在主观性强、重复性差及效率低等不足。近年来,随着图像识别、深度学习与多模态数据融合等技术的发展,AI在医学领域的应用快速拓展,为DED的早期筛查、精准诊断、个体化治疗及长期随访提供了全新技术路径。本文以技术为导向,系统回顾了AI在DED临床管理各环节中的最新研究进展,深入分析当前面临的技术局限与临床应用瓶颈,重点阐述AI在提升诊断客观性与标准化水平、优化个体化治疗策略、构建智能化监测随访体系等方面的重要价值与应用前景。同时,本文对AI辅助DED管理的未来发展趋势进行展望,旨在为推进DED诊疗的智能化转型与精准医学实践提供参考。

关键词: 人工智能, 干眼, 深度学习, 机器学习, 多模态数据

Abstract: Artificial intelligence(AI)mimics human cognitive processes to facilitate data analysis and support decision-making. Dry eye disease(DED)is a common chronic disorder of the ocular surface, and conventional diagnosis and treatment rely on manual judgement, which is often subjective, unreliable, and inefficient. However, recent advances in image recognition, deep learning and multimodal data fusion have expanded the role of Al in medicine. This offers new approaches for the early screening, accurate diagnosis and personalised treatment of DED, as well as its long-term follow-up.This technology-focused review summarises recent advances in AI across all stages of DED clinical management. It analyses current technical limitations and implementation challenges, and highlights the value of Al in enhancing diagnostic objectivity, optimising individualised therapeutic strategies, and establishing intelligent monitoring systems. It also discusses future directions in Al-assisted DED management to guide the intelligent transformation of DED care and promote the practice of precision medicine.

Key words: Artificialintelligence, Dry eye disease, Deep learning, Machine learning, Multimodal data

中图分类号: 

  • R771.3
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