Moving artificial intelligence from research laboratories into clinical practice requires rigorous validation and appropriate regulatory oversight. AI technologies must demonstrate reliability, safety, clinical relevance, and performance across diverse patient populations before they can be responsibly integrated into routine ophthalmic workflows.
This session will explore the clinical validation of AI-based diagnostic and decision-support technologies. Discussions will address study design, external validation, performance evaluation, regulatory pathways, post-deployment monitoring, interoperability, and integration with existing healthcare systems.
The session will also examine practical barriers to clinical adoption, including clinician trust, workflow compatibility, infrastructure requirements, cost, training, and accountability. Experts will discuss strategies for translating promising AI research into safe, scalable, evidence-based clinical applications.