Biomedical Imaging's Real Bottleneck Is Data Access, Not AI Models

A Databricks blog post argues that the main obstacle to AI in biomedical imaging is access to data, not the models themselves.

Imaging data is locked in clinical systems and is hard for hospitals, academic centers, medtech firms, and pharma companies to share. The post cites the EXAM study across 20 institutions, which found that federated learning, where model weights are shared rather than patient data, improved AUC by 16% on average. The post also says collaboration remains hard because of scanner and protocol heterogeneity, privacy governance, and the lack of a common data foundation. The claims come from the company's own blog and rest on the cited study's reported results.