AI Image Generation Technology in Ophthalmology: Use, Misuse and Future Applications
Abstract
BACKGROUND
AI-powered image generation technology holds the potential to dramatically reshape clinical ophthalmic practice. The adoption of this technology relies on clinician acceptance, yet it is an unfamiliar technology for both ophthalmic researchers and clinicians. In this work we present a literature review on the application of image generation technology in ophthalmology to discuss its theoretical applications and future role.
METHODS
First, we explore the key model designs used for image synthesis, including generative adversarial networks, autoencoders, and diffusion models. We then perform a survey of the literature for image generation technology in ophthalmology prior to September 2024, collecting the type of model used, as well as its clinical application, for each study. Finally, we discuss the limitations of this technology, the risks of its misuse and the future directions of research in this field.
RESULTS
Applications of this technology include improving diagnostic model performance, inter-modality image transformation, treatment and disease prognosis, image denoising, and education. Key challenges for integration of this technology into ophthalmic clinical practice include bias in generative models, risk to patient data security, computational and logistical barriers to model development, challenges with model explainability, inconsistent use of validation metrics between studies and misuse of synthetic images. Looking forward, researchers are placing a…
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