Phipps, Benjamin;
Hadoux, Xavier;
Sheng, Bin;
Campbell, J Peter;
Liu, TY Alvin;
Keane, Pearse A;
Cheung, Carol Y;
... Van Wijngaarden, Peter; + view all
(2025)
AI image generation technology in ophthalmology: Use, misuse and future applications.
Progress in Retinal and Eye Research
, 106
, Article 101353. 10.1016/j.preteyeres.2025.101353.
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Abstract
Background: AI-powered image generation technology holds the potential to reshape medical practice, yet it remains an unfamiliar technology for both medical researchers and clinicians alike. Given the adoption of this technology relies on clinician understanding and acceptance, we sought to demystify its use in ophthalmology. To this end, we present a literature review on image generation technology in ophthalmology, examining both its theoretical applications and future role in clinical practice. // Methods: First, we consider 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, presenting both the type of model used and its clinical application. 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 AI diagnostic models, inter-modality image transformation, more accurate treatment and disease prognostication, image denoising, and individualised education. Key barriers to its adoption include bias in generative models, risks to patient data security, computational and logistical barriers to development, challenges with model explainability, inconsistent use of validation metrics between studies and misuse of synthetic images. Looking forward, researchers are placing a further emphasis on clinically grounded metrics, the development of image generation foundation models and the implementation of methods to ensure data provenance. // Conclusion: Compared to other medical applications of AI, image generation is still in its infancy. Yet, it holds the potential to revolutionise ophthalmology across research, education and clinical practice. This review aims to guide ophthalmic researchers wanting to leverage this technology, while also providing an insight for clinicians on how it may change ophthalmic practice in the future.
| Type: | Article |
|---|---|
| Title: | AI image generation technology in ophthalmology: Use, misuse and future applications |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.preteyeres.2025.101353 |
| Publisher version: | https://doi.org/10.1016/j.preteyeres.2025.101353 |
| Language: | English |
| Additional information: | Copyright © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Generative AI; Artificial intelligence; Deep learning; Ophthalmology; Image generation; Generative adversarial networks; GAN; Autoencoders; Diffusion models; AI diagnostic models; Data augmentation; Image denoising; Bias in AI; Patient data security; Deepfake; Blockchain; Multimodal imaging; Foundation model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10206884 |
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