Ulas Bagci delivers a keynote at ETRA 2026
The talk traced a decade of research using radiologist gaze to inform medical AI, from image segmentation to collaboration between clinicians and AI systems.
EYES WIDE OPEN: A DECADE OF GAZE-GUIDED MEDICAL INTELLIGENCE
Every diagnostic decision begins with where a clinician’s eyes land — yet for decades, this rich cognitive signal has been discarded. In this talk, I trace a ten-year arc of research transforming radiologist gaze from passive behavioral data into an active computational signal that fundamentally reshapes how AI systems learn, segment, and diagnose.
Beginning with Gaze2Segment (2016) and C-CAD (2019), which first demonstrated that fixation patterns encode expert knowledge transferable to deep networks, I show how this idea matured through GazeSAM and GazeGNN (2023–2024) into real-time, registration-free integration with foundation models — eliminating the preprocessing bottleneck that long prevented clinical deployment.
I then present our latest systems — EyeSee, GazeMind, EyeTune, and GazeAssist (2025–2026) — which close the loop entirely: AI no longer just consumes gaze but predicts, validates, and augments it, creating a bidirectional human-AI cognitive partnership.
Across several systems we developed, one principle endures: the most powerful signal in medical AI was never in the pixels — it was in the eyes reading them.
