#ai validation gap

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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.

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How Real-World Imaging Data Can Close the AI Validation Gap - Segmed

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.