Understanding the AI Validation Gap in Imaging - Segmed
Imaging AI models are commonly developed using retrospective datasets that are carefully curated to optimize image quality by minimizing noise and variability. While these datasets are essential for early algorithm development, they rarely reflect how imaging is acquired and interpreted in routine clinical practice. In the real world, imaging data are heterogeneous by design. Differences in scanner manufacturers, reconstruction algorithms, acquisition protocols, and patient populations introduce variability that curated datasets often exclude. When models are validated only under homogeneous conditions, they risk learning dataset-specific shortcuts rather than clinically meaningful signals.
