Clinical decision-making in ophthalmology often requires the integration of imaging findings, patient history, clinical measurements, laboratory information, and longitudinal data. Artificial intelligence can provide computational support by identifying patterns across these diverse sources and generating information that may assist clinicians during evaluation and management.
This session will explore AI-powered clinical decision-support systems designed to assist with diagnosis, risk stratification, treatment planning, referral decisions, and disease monitoring. Discussions will focus on how intelligent systems can integrate multimodal information while presenting clinically relevant insights in an understandable and actionable manner.
The session will also examine validation, explainability, workflow integration, clinical responsibility, and patient safety. Experts will discuss the importance of maintaining clinician oversight and developing decision-support technologies that enhance efficiency and consistency without compromising individualized patient care.