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Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges.

Sadi Can Sonmez¹, Mertcan Sevgi^2,3, Fares Antaki^2,3,4

¹Department of Public Health, Ege University, Izmir, Turkey.

The British Journal of Ophthalmology

|June 26, 2024

Summary

This summary is machine-generated.

Generative artificial intelligence, including advanced models, offers new ways to create synthetic medical images for ophthalmology training and diagnostics. Challenges like data bias and clinical implementation need addressing for widespread adoption.

Keywords:

Diagnostic tests/Investigation Medical Education Prognosis Public health

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Generative artificial intelligence (AI) is rapidly advancing.
AI technologies are poised to transform the medical sector, especially ophthalmology.
Synthetic data generation is a key application.

Purpose of the Study:

To explore the impact of generative AI on ophthalmology.
To highlight the potential of AI in creating synthetic medical images.
To discuss the applications of multimodal foundational models in eye care.

Main Methods:

Utilizing generative adversarial networks (GANs) and diffusion models for synthetic image creation.
Leveraging multimodal foundational models for diverse data generation (images, text, video).
Analyzing potential applications in diagnostics, education, and professional training.

Main Results:

Generative AI can produce synthetic images to train specialized deep learning models.
Multimodal models offer a wide range of applications in ophthalmology.
Potential to enhance diagnostic accuracy and improve patient education.

Conclusions:

Generative AI holds significant promise for advancing ophthalmology.
Current challenges include data bias, safety, and clinical integration.
Further research and development are needed for practical implementation.