Comprehensive Summary
This article explores the use, misuse, and future applications of AI-powered image generation technologies in ophthalmology. The authors surveyed literature published prior to September 2024, focusing on key generative model architectures such as generative adversarial networks (GANs), autoencoders, and diffusion models. Applications identified include enhancing AI diagnostic models, transforming images between modalities (e.g., from color fundus to fluorescein angiography), predicting treatment outcomes, denoising images, and supporting individualized medical education. However, the paper highlights major barriers to adoption, including bias in generated data, privacy and security risks, computational demands, lack of model explainability, inconsistent validation metrics, and risks of synthetic image misuse. Future research directions emphasize the use of clinically grounded metrics, development of foundation models, and techniques to ensure data provenance.
Outcomes and Implications
The findings hold significance because ophthalmology, as an increasingly image-dependent specialty, stands to benefit significantly from advances in generative AI. By enhancing the quality and accessibility of diagnostic and educational imaging, this technology could transform clinical workflows, improve diagnostic accuracy, and personalize treatment planning. Although still at an early stage, clinical implementation may be feasible in the near future if critical barriers such as model bias, data privacy, validation rigor, and computational limitations are addressed. The authors emphasize the need for continued research focused on clinical relevance, external validation, and ethical safeguards to support its safe integration into clinical practice.