Sharing medical imaging data is the main AI bottleneck

Most FDA-approved AI in healthcare focuses on radiology, but the biggest challenge isn’t the models—it’s safely sharing and standardizing medical imaging data. Hospital data is siloed, deeply regulated, and hard to de-identify, making research and model validation difficult. Even top academic models rarely publish code or repeatable data. Device makers and pharma also struggle to generalize AI, since imaging data varies so widely between systems and sites.

• Radiology dominates FDA-approved medical AI devices
• Most imaging AI is human-supervised, not fully automated
• Clinical data is locked in proprietary archives
• Reproducibility is limited by lack of shared datasets
• Vendors and trials struggle with data variability