AI in Eye Care and the Practical Path to Better Patient Care

Dr. Chris Lievens remembers looking at a retinal photograph and seeing little cause for concern. An artificial intelligence system had flagged more than mild diabetic retinopathy, but the findings were not apparent to him. Then he saw the fluorescein angiography. The additional imaging supported the AI’s assessment, changing his understanding of what the technology might contribute to patient care. That experience opens a broader conversation about AI in eye care on the Four Eyed Professor podcast, where Dr. Chris Lievens joins Dr. Scott Morris and ophthalmologist Dr. Rehan Ahmed, co-editors of AI in Eye Care . Their discussion explores how artificial intelligence could expand clinical insight, reduce administrative work, and help clinicians spend more time connecting with patients. It also offers a practical starting point for professionals unsure where the technology belongs in their practices. Topics Covered : Expanding What the Eye Can Reveal Finding Value in Everyday Practice Operations Supporting Clinical Thinking and Human Connection Building Adoption Around Data and Clear Expectations Expanding What the Eye Can Reveal Much of the interest in clinical AI begins with imaging. Retinal photographs, OCT scans, and visual fields provide information that can be examined for patterns, making them natural subjects for the conversation. Yet the potential extends beyond helping clinicians recognize familiar signs of ocular disease. For Dr. Rehan Ahmed, one of the most compelling areas is oculomics, which explores how information from the eye might reveal broader health signals. Rather than looking at a fundus image only for retinal disease, researchers and developers are investigating what it could suggest about cardiovascular or neurodegenerative conditions. The appeal lies partly in the possibility of earlier intervention. If an image could help identify someone at increased risk for a future cardiovascular event, that information might create an opportunity for preventive care. Throughout the discussion, these applications are presented as areas of promise, with questions still surrounding how they will become part of everyday practice. More information also introduces difficult choices. Dr. Chris Lievens asks whether patients would want to learn about a condition they cannot prevent or treat. While acknowledging that concern, Dr. Rehan Ahmed points to the potential value of clinical trial participation and a better understanding of disease. These possibilities connect with a broader framework offered by Dr. Scott Morris: clinical AI encompasses imaging, visual function, and clinical decision-making. Advances in understanding ocular structures may lead to new questions about function, which can then inform how clinicians interpret findings and approach care. Finding Value in Everyday Practice Operations While diagnostic possibilities attract attention, the most accessible starting point may be the work surrounding the examination. Scheduling, documentation, referral letters, prior authorizations, and insurance processes consume time that could otherwise support patient care. A simple calculation illustrates the opportunity. Dr. Scott Morris describes a clinician seeing 15 patients for 20 minutes each during an eight-hour day. Those encounters account for five hours, leaving a substantial portion