Technology, AI and Digital Ophthalmology

Digital tools now shape how we detect, decide, and deliver care. This session explains how to build trustworthy imaging AI, deploy teleophthalmology at scale, and weave sensors, software, and analytics into clinics without adding friction. We start with data foundations—image quality, labeling standards, and governance—then move to model development, validation, calibration, and post-deployment monitoring. You’ll learn how to design decision-support that assists rather than distracts, integrate algorithms into EHR and imaging systems, and measure impact with safety, equity, and cost in mind. Technology, AI & Digital Ophthalmology also covers remote triage, home monitoring (vision, IOP, OCT-like signals), and patient-held data from wearables and smart lenses. For product teams, we outline human-factors studies, cybersecurity, and software lifecycle documentation; for clinicians, we break down practical workflows that cut time-to-answer while preserving judgment and accountability. If you’re searching for a Vision Conference to present models, apps, or device analytics, this page clarifies evidence needs—from reader studies and non-inferiority designs to prospective impact trials and real-world model drift control. We discuss fairness by design, domain shift, and multilingual deployment, plus procurement questions hospitals should ask before buying AI. Finally, we link digital endpoints to therapeutics and surgery planning, so images and logs become interventions that change outcomes. Along the way, we highlight teleophthalmology as the backbone for equitable access, connecting rural settings, school and workplace programs, and home follow-up to specialty care with transparent escalation rules and closed-loop communication.