Artificial Intelligence in Ophthalmology: Innovations and Clinical Impact

Artificial intelligence (AI) is poised to revolutionize ophthalmology, bringing transformative capabilities to both research and clinical care. With rapid advancements in machine learning, deep learning, and other AI-driven approaches, we are witnessing significant progress in disease detection, diagnosis, treatment planning, and patient management. AI-based tools are being increasingly integrated into ophthalmic imaging, automated screening programs, predictive analytics, and real-time clinical decision support. These innovations offer the promise of enhanced accuracy, efficiency, and accessibility in patient care while introducing new challenges surrounding implementation, validation, and equity in diverse practice settings.

This Research Topic aims to gather cutting-edge research and reviews that explore the full spectrum of AI applications in ophthalmology. We welcome articles addressing, but not limited to, the following themes:

- AI for surgical planning, outcome prediction, detection of corneal pathologies, and refractive error analysis
- AI-based risk assessment, automated visual field analysis, optic nerve imaging, and prediction of disease progression
- Machine learning in the diagnosis, grading, and monitoring of uveitis and other inflammatory conditions
- Automated cataract detection and grading, AI for intraocular lens (IOL) calculations, and surgical guidance
- AI for early detection and classification of neuro-ophthalmic pathologies, visual pathway analysis, and outcome prediction
- Integrations of AI with teleophthalmology, mobile health, wearable devices, and other digital health solutions
- Applications of AI in imaging and diagnosis of orbital and oculoplastic conditions, outcome prediction, and trauma assessment
- AI tools for early screening, diagnosis, and monitoring of pediatric eye diseases and strabismus
- Deep learning for retinal disease detection, automated grading of diabetic retinopathy, macular degeneration, and retinal image analysis
- AI-driven intraoperative guidance, surgical simulation, outcome prediction, and complication risk assessment

We also welcome interdisciplinary perspectives on challenges and opportunities in deploying AI technologies, including data privacy, ethical and regulatory issues, and strategies for successful clinical integration. By uniting diverse contributions, this Research Topic aims to provide a comprehensive overview of how AI is shaping every facet of ophthalmology and advancing patient outcomes.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: ophthalmology, eye, AI, artificial intelligence, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.