The Makings Of 'Regulatory-Grade' Real-World Data In Ophthalmology Using AI

In recent years, regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have increasingly emphasized the importance of incorporating real-world data (RWD) into clinical research and regulatory decision-making processes. This shift reflects a growing recognition of RWD’s potential to enhance drug development, support more informed regulatory evaluations, and ultimately improve patient outcomes. As a result, there is a heightened focus on identifying and utilizing RWD that meets stringent regulatory standards to ensure its reliability and relevance for critical applications such as drug approvals and postmarket safety monitoring. Because it reflects the complexities and variability of real-world healthcare delivery, RWD offers a more holistic and nuanced understanding of how medical products perform across diverse patient populations and care environments.