Research progress of generative artificial intelligence for ophthalmic imaging and clinical applications
10.3980/j.issn.1672-5123.2026.10.25
- VernacularTitle:生成式人工智能在眼科影像与临床应用中的研究进展
- Author:
Zhuo HUANG
1
;
Yali DU
1
Author Information
1. Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong;Fifth Clinical Institute of Shantou University Medical College;Guangdong Engineering Technology Research Center of Precision Treatment for Ocular Diseases;Guangdong Engineering Research Center of Intelligent Diagnosis and Treatment for Ocular Diseases;Shantou Key Laboratory of Ocular Disease Prevention, Treatment, and Research, Shantou 515041, Guangdong Province, China
- Publication Type:Journal Article
- Keywords:
generative artificial intelligence;
ophthalmology;
generative adversarial networks;
diffusion models;
large language models
- From:
International Eye Science
2026;26(10):1838-1843
- CountryChina
- Language:Chinese
-
Abstract:
Generative artificial intelligence(Gen-AI), an important branch of artificial intelligence(AI), has attracted increasing attention in medical image analysis and clinical decision support.Different from traditional discriminative models, Gen-AI can generate high-quality and diverse new data by learning the underlying distribution features of data. It exhibits unique advantages in alleviating insufficient training samples, mitigating uneven data distribution, and improving the generalization ability of models. Ophthalmology is highly dependent on multimodal imaging, making it a particularly suitable field for the application of Gen-AI. This review summarizes recent advances in major Gen-AI techniques, including generative adversarial networks, diffusion models, and large language models, and discusses their applications in ophthalmic disease screening and diagnosis, image synthesis and data augmentation, prognostic prediction, and individualized treatment planning. It also analyzes challenges in the course of clinical translation, including data quality, model credibility, ethical supervision and other issues, so as to provide references for its standardized application.
