Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
This addresses the problem of ensuring AI-generated medical images are interpretable for radiologists, though it appears incremental as it focuses on quantifying gaze differences without major methodological breakthroughs.
The study analyzed radiologists' eye movements to understand their diagnostic strategies and found that gaze behavior shifts when viewing real versus AI-generated medical images, with differences in fixation bias maps and saccades patterns.
Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first analyze radiologists' attention and agreement by measuring the distribution of various eye-movement patterns, including saccades direction, amplitude, and their joint distribution. These metrics help uncover patterns in attention allocation and diagnostic strategies. Furthermore, we investigate whether and how doctors' gaze behavior shifts when viewing authentic (Real) versus deep-learning-generated (Fake) images. To achieve this, we examine fixation bias maps, focusing on first, last, short, and longest fixations independently, along with detailed saccades patterns, to quantify differences in gaze distribution and visual saliency between authentic and synthetic images.