Single-Center vs Multi-Center Studies: What We've Learned About Scaling Imaging AI
A model that scores 0.95 AUC at the institution where it was trained may not hold that performance at the hospital down the road. The reason is structural: single-center datasets encode institutional fingerprints (scanner-specific noise, local acquisition protocols, narrow referral patterns) that algorithms learn alongside the pathology they're supposed to detect. When those signals disappear in a new environment, so does performance. Multi-center validation isn't a regulatory checkbox. It's the mechanism that separates optimization from generalizability. Our latest piece breaks down what changes when imaging AI development moves from controlled single-center conditions to real-world, multi-site data and what we've learned working with globally distributed datasets at scale.
