Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities

Abstract

ABSTRACT Introduction Demand in clinical services within the field of ophthalmology is predicted to rise over the future years. Artificial intelligence, in particular, machine learning-based systems, have demonstrated significant potential in optimizing medical diagnostics, predictive analysis, and management of clinical conditions. Ophthalmology has been at the forefront of this digital revolution, setting precedents for integration of these systems into clinical workflows. Areas covered This review discusses integration of machine learning tools within ophthalmology clinical practices. We discuss key issues around ethical consideration, regulation, and clinical governance. We also highlight challenges associated with clinical adoption, sustainability, and discuss the importance of interoperability. Expert opinion Clinical integration is considered one of the most challenging stages within the implementation process. Successful integration necessitates a collaborative approach from multiple stakeholders around a structured governance framework, with emphasis on standardization across healthcare providers and equipment and software developers.