Accuracy is not enough:
Calibration & Reproducibility.

Research note · October 2026

A medical AI result is more useful when researchers can trace it to a dataset version, split protocol, model artifact, execution environment, and statistical procedure. Modern research increasingly treats uncertainty and reproducibility as first-class evaluation dimensions.

Beyond one metric

Depending on the task, a benchmark can include AUROC, AUPRC, sensitivity, specificity, calibration error, Brier score, negative log likelihood, confidence intervals, and external-validation performance.

Why provenance matters

Changing a preprocessing filter, patient split, or augmentation strategy can change the scientific meaning of a result. Neumage therefore records experiment specifications, checksums, execution targets, and evidence artifacts alongside results.

The broader research landscape

MICCAI 2026 explicitly includes evaluation and benchmarking, uncertainty quantification, domain adaptation, reproducibility, reliability, and multimodal models among its research areas. This reflects a shift from asking only whether a model works to asking under which conditions it works and how reliably the result can be reproduced.