OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System One

OmniMed-Jev is a new medical multimodal model that represents each decision as a Choice, Noul, or Score over a runtime-supplied candidate set, returning a full probability distribution for each decision. By unifying diverse imaging modalities and prediction tasks into a single candidate-conditioned probability model, it makes heterogeneous outputs comparable probabilities rather than task-specific strings. In controlled comparisons against a generative baseline, OmniMed-Jev’s reported probabilities align more closely with observed correctness, reducing calibration error by up to an order of magnitude and reliability error by up to two, while maintaining comparable point-prediction performance.

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.

• multimodal