How AI Is Shaping The Creation Of 'Regulatory-Grade' Real-World Data In Ophthalmology

By Theodore Leng, MD, MS, Verana Health

Regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are increasingly recognizing the value of real-world data (RWD) in shaping clinical research and informing regulatory decisions. This growing emphasis reflects a broader shift in the healthcare landscape, where RWD—derived from sources like electronic health records, insurance claims, and patient registries—is being leveraged to complement traditional randomized controlled trials. However, a critical challenge remains: converting this vast and often unstructured data into 'regulatory-grade' evidence that meets stringent standards.

In the field of ophthalmology, this transformation is particularly impactful. As the specialty grapples with complex and prevalent conditions such as cataracts and neovascular age-related macular degeneration (nAMD), the use of RWD is opening new avenues for innovation. AI-driven analysis of ophthalmic data can uncover patterns and insights that were previously difficult to detect, enabling more precise diagnostics, personalized treatment strategies, and robust evidence generation. These developments not only support more informed regulatory decision-making but also pave the way for enhanced patient care, making the integration of RWD and AI a cornerstone of modern ophthalmic research and practice.