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arXiv Machine Learning By Xingrui Gu, Hanxue Gu, Yuxiang Zhang, Yang Yang

Patient, Place, Prior (P$^3$): What Counts as Personalization in Medical World Models?

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The paper introduces Patient, Place, Prior (P³), an audit framework that evaluates whether a longitudinal medical imaging forecast benefits from a patient’s own imaging history (Patient), from patient‑matched spatial support (Place), and from predictive value beyond a population‑average model (Prior). It applies this framework to Cancer JEPA, a one‑step model that predicts future breast dynamic contrast‑enhanced MRI scans during neoadjuvant therapy, incorporating a lesion‑constrained neural correction trained with an occlusion‑based latent objective. Validation shows lower forecast error when the neural correction uses the patient’s own history and matched lesion maps, though the 95% interval comparing patient‑specific to population‑average corrections includes zero, illustrating that P³ distinguishes input usage from true patient‑specific predictive advantage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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