Artificial Intelligence And Machine Learning In The Early Diagnosis Of Corneal And Ocular Surface Diseases

This study aims to explore the current applications of artificial intelligence (AI) in supporting the diagnosis and management of corneal and ocular surface diseases. By analyzing recent advancements, we assess AI's role in enhancing diagnostic accuracy, efficiency, and personalized patient care.

Hospital Clinico San Carlos

A systematic literature review was performed to evaluate AI and ML in corneal disease diagnosis and management. Searches were conducted in PubMed using key terms like “artificial intelligence,” “machine learning,” “deep learning,” and “corneal diseases.”

Studies published in English over the past ten years were included, focusing on AI-based diagnostics, prognosis, and treatment. Exclusion criteria included case reports, non-original data, and limited AI integration. Data extraction examined AI methodologies, imaging modalities, diagnostic accuracy, and clinical applicability.

This review synthesizes current evidence on AI’s role in corneal disease management, identifying gaps and future research directions.

AI has revolutionized corneal disease diagnosis, improving accuracy, efficiency, and early detection. AI-based machine learning (ML) models analyze vast imaging datasets, identifying subtle patterns undetectable by traditional methods. This is particularly effective for subclinical keratoconus, Fuchs' dystrophy, and other corneal pathologies. AI enhances diagnostic precision, workflow efficiency, and personalized patient care. Continuous learning allows AI models to refine predictions, reducing variability and improving outcomes. Despite challenges like data standardization and ethical concerns, AI is transforming ophthalmology into a data-driven, predictive field, optimizing diagnostics and treatment strategies.

AI’s integration into corneal disease management represents a paradigm shift in ophthalmology, offering unprecedented precision and efficiency. By enabling early detection and personalized assessments, AI minimizes diagnostic errors and accelerates clinical workflows. Its continuous evolution fosters more reliable, data-driven decision-making, improving patient care and treatment outcomes. However, to fully harness its potential, collaboration among researchers, clinicians, and policymakers is essential to address standardization, regulatory, and ethical challenges. As AI technologies advance, they will play an increasingly central role in modern ophthalmology, setting new standards for diagnosis and therapeutic strategies.