Good Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Models

ArXi:2604.04239v1 Announce Type: cross Multimodal deep learning models that fuse whole-slide histopathology images with genomic data have achieved strong discriminative performance for cancer survival prediction, as measured by the concordance index. Yet whether the survival probabilities derived from these models - either directly from native outputs or via standard post-hoc reconstruction - are calibrated remains largely unexamined.