AI RESEARCH
A World Model of Radiologist Reading for Medical Image Representation Learning
arXiv CS.AI
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ArXi:2605.23992v1 Announce Type: cross Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it.