How Real-World Imaging Data Can Close the AI Validation Gap | Segmed Experts
Artificial intelligence (AI) has demonstrated impressive performance across a range of medical imaging tasks, from lesion detection and segmentation to prognostication and treatment response assessment. However, most imaging AI systems are still trained and validated using limited and highly curated datasets. Such datasets are often drawn from a small number of institutions, scanners, and patient populations. While these datasets are essential throughout the algorithm development process, they rarely reflect the complexity, heterogeneity, and unpredictability of real-world clinical practice. High performance on internal validation does not guarantee reliable performance in real-world clinical settings. This disconnect is often referred to as the AI validation gap . It has become one of the central barriers to trustworthy clinical AI. Closing this gap requires validating models against real-world imaging data that reflect true deployment conditions, enabling robust, fair, a...
