Improving Glaucoma Care with Agentic AI

This project will develop advanced AI tools for clinicians that combine imaging, functional testing, and clinical measurements to help them improve glaucoma care and preserve vision.

This project will develop advanced AI tools that combines imaging, functional testing, and clinical measurements over time to understand glaucoma progression. By providing clear, automated summaries for doctors, these tools aim to improve diagnosis and staging, guide treatment decisions, and reduce the documentation burden in glaucoma care.

This proposal introduces the first agentic AI framework that integrate structural, functional, and clinical data to generate interpretable summaries of patient changes over time. Unlike prior models that analyze single visits or modalities, these systems capture temporal change and cross-modal relationships over time. The use of agentic AI for autonomous data synthesis and narrative generation represents a new paradigm in ophthalmic AI, improving interpretability, workflow efficiency, and clinical utility.

The proposed research will advance glaucoma management by developing agentic AI systems that integrate imaging, functional testing, and clinical data across multiple visits. By combining structural, functional, and temporal information, these models will more accurately identify patterns of glaucomatous progression and their relationship to risk factors such as intraocular pressure and treatment response. This work will improve diagnosis and staging, support personalized care, and reduce documentation burden on clinicians through automated generation of clear, interpretable summaries.