Generative artificial intelligence in ophthalmology

生成语法 眼科 验光服务 医学 计算机科学 人工智能
作者
Ethan Waisberg,Joshua Ong,Sharif Amit Kamran,Mouayad Masalkhi,Phani Paladugu,Nasif Zaman,Andrew G. Lee,Alireza Tavakkoli
出处
期刊:Survey of Ophthalmology [Elsevier BV]
卷期号:70 (1): 1-11 被引量:38
标识
DOI:10.1016/j.survophthal.2024.04.009
摘要

Generative artificial intelligence (AI) has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep learning framework that has become a powerful technique in medicine, particularly in ophthalmology for image analysis. In this paper we review the current ophthalmic literature involving GANs, and highlight key contributions in the field. We briefly touch on ChatGPT, another application of generative AI, and its potential in ophthalmology. We also explore the potential uses for GANs in ocular imaging, with a specific emphasis on 3 primary domains: image enhancement, disease identification, and generating of synthetic data. PubMed, Ovid MEDLINE, Google Scholar were searched from inception to October 30, 2022, to identify applications of GAN in ophthalmology. A total of 40 papers were included in this review. We cover various applications of GANs in ophthalmic-related imaging including optical coherence tomography, orbital magnetic resonance imaging, fundus photography, and ultrasound; however, we also highlight several challenges that resulted in the generation of inaccurate and atypical results during certain iterations. Finally, we examine future directions and considerations for generative AI in ophthalmology.
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