Medical images contain far more information than is typically used for a single diagnostic task. I will present our work on learning generalizable, patient-specific representations from routine chest imaging, tracing a progression from task-specific prediction to multimodal foundation models and spatially grounded world models. These results point toward medical AI systems that move beyond task specific prediction, with applications in risk assessment, spatial reasoning, and image-guided care
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