Advancements in artificial intelligence for the diagnosis and management of anterior segment diseases.

Abstract

Purpose of review: The integration of artificial intelligence (AI) in the diagnosis and management of anterior segment diseases has rapidly expanded, demonstrating significant potential to revolutionize clinical practice.

Recent findings: AI technologies, including machine learning and deep learning models, are increasingly applied in the detection and management of a variety of conditions, such as corneal diseases, refractive surgery, cataract, conjunctival disorders (e.g., pterygium), trachoma, and dry eye disease. By analyzing large-scale imaging data and clinical information, AI enhances diagnostic accuracy, predicts treatment outcomes, and supports personalized patient care.

Summary: As AI models continue to evolve, particularly with the use of large models and generative AI techniques, they will further refine diagnosis and treatment planning. While challenges remain, including issues related to data diversity and model interpretability, AI's integration into ophthalmology promises to improve healthcare outcomes, making it a cornerstone of data-driven medical practice. The continued development and application of AI will undoubtedly transform the future of anterior segment ophthalmology, leading to more efficient, accurate, and individualized care.