Anti-leakage evaluation for reproducible medical AI

In medical AI, a strong number can hide a weak experimental question. This note frames anti-leakage evaluation as a prerequisite for reproducible and trustworthy systems.

The practical direction is to document data manifests, split data by the right real-world constraints, and report failure modes instead of only the best metric.

Initial notes on temporal shortcuts, information leakage, and more honest evaluation protocols for medical AI systems.

Medical-ai

Anti-leakage evaluation for reproducible medical AI