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AI image generation technology in ophthalmology: Use, misuse and future applications
Benjamin Phipps1, Xavier Hadoux1, Bin Sheng2
1Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, 3002, VIC, Australia; Ophthalmology, Department of Surgery, University of Melbourne, Parkville, 3010, VIC, Australia.
Progress in Retinal and Eye Research
|March 19, 2025
Summary
AI image generation is emerging in ophthalmology, offering potential for research and clinical practice. This review demystifies its applications, challenges, and future directions for clinicians and researchers.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- AI-powered image generation is novel for medical professionals.
- Understanding and acceptance are crucial for technology adoption in clinical settings.
- This review focuses on demystifying AI image generation in ophthalmology.
Purpose of the Study:
- To review the literature on AI image generation technology in ophthalmology.
- To examine theoretical applications and the future role of this technology.
- To provide guidance for ophthalmic researchers and insights for clinicians.
Main Methods:
- Review of key image synthesis models: generative adversarial networks, autoencoders, and diffusion models.
- Survey of ophthalmology literature on image generation technology up to September 2024.
- Discussion of limitations, risks, and future research directions.
Main Results:
- Applications include enhancing AI diagnostics, inter-modality transformation, treatment prognostication, image denoising, and personalized education.
- Barriers include model bias, data security risks, computational challenges, explainability issues, inconsistent validation, and misuse of synthetic images.
- Future research emphasizes clinically grounded metrics, foundation models, and data provenance.
Conclusions:
- AI image generation is nascent in ophthalmology but has revolutionary potential.
- The technology can transform ophthalmic research, education, and clinical practice.
- This review serves as a guide for leveraging AI image generation in ophthalmology.
