NeuroVFM learns from 5.24 million routine CT and MRI series

The peer-reviewed NeuroVFM study trained a three-dimensional visual foundation model on 5.24 million uncurated clinical CT and MRI series. Authors report competitive diagnosis, triage and preliminary report-generation performance and propose domain-specific imaging models as grounded modules for agentic clinical systems.

Impact: Learning from real operational data may produce more clinically grounded representations than internet-only training. Deployment still requires privacy controls, uncertainty calibration, workflow testing and prospective multi-system validation.