The utility of artificial intelligence in ophthalmic clinical trials

Purpose of review The current article provides an overview of the utility of artificial intelligence approaches to aid in the design, recruitment, execution, and dissemination of ophthalmic clinical trials. Recent findings Within the last decade, artificial intelligence has heralded a new age for ophthalmology, with novel applications habitually appearing within the literature. Though clinical trials are considered the gold standard for driving evidence-based practice, remarkably few studies have examined the potential for machine learning to augment the clinical trial pipeline. Clinical trials within ophthalmology often do not reach planned endpoints due to insufficient enrolment, cost overruns, and can lack reliability from unblinded outcome assessors. Ones that do, frequently take longer to enroll patients than intended. Artificial intelligence-based approaches have recently been shown to be effective in identifying eligible clinical trial participants using both imaging and text data. Summary Given the key role of clinical trials in the advancement of ophthalmic clinical practice, trialists should consider the potential for artificial intelligence-powered tools to enhance the design, recruitment, and delivery of future studies.