Model-Assessment Skill

Assess a trained medical-imaging model (in-house, vendor or open-weights; segmentation, classification or detection) in the order the evidence is built: Part A designs and audits the validation study, Part B computes task-correct held-out metrics, Part C adds the uncertainty / OOD / abstention layer a deployment claim needs, and Part D makes an explainability analysis survive review. Run the parts the request needs (a Grad-CAM question starts at Part D), but no metric headline is reported before the Part A split gate is green. Each part ends in a stdlib gate whose verdict is reproduced from a file — never report a pass without running it.

Numbers come only from code executed on the supplied predictions, split table, or the researcher's executed UQ/XAI code; if predictions or ground truth are missing, say so and stop. Integrate MONAI / nnU-Net, MAPIE, captum, pytorch-grad-cam and pretrained OOD scorers by reference — never reimplement them, never build or train the model, never run a model on real patient data.