Generative artificial intelligence in ophthalmology
Year: 2024
Type: article
Abstract: 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.
Source: Survey of Ophthalmology
Institutions: University of Cambridge, University of Michigan, Michigan Medicine, University College Dublin, Brigham and Women's Hospital, Harvard University, Thomas Jefferson University, University of Nevada, Reno, University of Iowa, Houston Methodist, The University of Texas MD Anderson Cancer Center, Baylor College of Medicine, University of Iowa Hospitals and Clinics, Weill Cornell Medicine, The University of Texas Medical Branch at Galveston, Texas A&M University
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,770
Award: G3795089887
OpenAlex ID: W4396952102
