Challenges and countermeasures in the development of artificial intelligence research in ophthalmology

Abstract

The advent of artificial intelligence (AI) technology has led to revolutionary advancements in the diagnosis and treatment of ophthalmic diseases, introducing a novel AI-assisted diagnostic approach for ophthalmology that is rich in imaging diagnostic technologies. However, as clinical applications continue to evolve, AI research in ophthalmology faces challenges such as the lack of standardized datasets and innovative algorithm models, insufficient cross-modal information fusion, and limited clinical interpretability. In response to the growing demand for AI research in ophthalmology, it is essential to establish ophthalmic data standards and sharing platforms, innovate core algorithms, and develop clinical logic interpretable models for the screening, diagnosis, and prediction of eye diseases. Additionally, the deep integration of cutting-edge technologies such as 5G, virtual reality, and surgical robots would advance the development of ophthalmic intelligent medicine into a new phase.