[关键词]
[摘要]
生成式人工智能(Gen-AI)作为人工智能(AI)领域的重要分支,近年来在医学影像分析与临床决策支持中受到广泛关注。与传统判别式模型不同,生成式AI通过学习数据的潜在分布特征,能够生成高质量、多样化的新数据,在缓解训练样本不足、改善数据分布不均及提升模型泛化能力等方面展现出独特优势。眼科学是一门高度依赖影像信息的学科,使其成为生成式AI的重要应用场景。文章综述了生成对抗网络、扩散模型及大语言模型等生成式AI核心技术的发展现状,总结其在眼科疾病筛查与诊断、影像合成与数据增强以及疾病预后预测与个体化治疗中的研究进展,并分析其临床转化过程中面临的数据质量、模型可信性及伦理监管等挑战,以期为其规范化应用提供参考。
[Key word]
[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.
[中图分类号]
[基金项目]
2025年中央引导地方科技发展专项资金(No.STKJ2025083)