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

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

Abstract:Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the prediction. A population-average forecast may be useful but cannot establish a patient-specific world-model claim. We introduce Patient, Place, Prior (P$^3$), an audit asking whether a forecast benefits from the patient's longitudinal imaging history (Patient), benefits from patient-matched externally supplied spatial support (Place), and gains predictive value beyond a population-average prediction under matched support and context (Prior). We also propose Cancer JEPA, a one-step model that forecasts frozen representations of future breast dynamic contrast-enhanced MRI examinations during neoadjuvant therapy. It adds a lesion-constrained neural correction, trained with an occlusion-based latent objective, to a patient-conditioned low-complexity reduced-rank regression baseline. This factorization permits a post-hoc P$^3$ audit of the frozen model. In a validation cohort previously used in development, forecast error is lower when the neural correction receives the patient's history rather than another patient's and patient-matched lesion occupancy maps rather than substituted maps. However, the descriptive 95% interval comparing the correction computed from patient history with the population-average neural correction includes zero. P$^3$ thus separates input use from evidence of patient-specific predictive value beyond a population-level pattern.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.09194 [cs.LG]

(or arXiv:2610.09194v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09194 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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