TL;DRParis-based Raidium launched its AI-native radiology platform at Moffitt Cancer Center. Its Curia model automates tumor tracking and cuts reader variability by 3x.
Raidium, a Paris and Silicon Valley-based radiology startup, has launched its AI-native imaging platform in the US at Moffitt Cancer Center, one of the country’s leading oncology research institutions. The platform, called Raidium Read, replaced Moffitt’s legacy radiomics applications and is currently available for clinical trials and research use. FDA 510(k) clearance is expected before the end of 2026.
The system is built around Curia, Raidium’s proprietary foundation model trained on over 200 million CT and MRI slices from 150,000 exams. Instead of layering AI tools on top of an existing PACS viewer, the company built the viewer itself from scratch with the model embedded. Curia performs organ-agnostic, automated RECIST measurements, the standardised method for tracking tumor response to treatment, across multiple time points. Raidium says this cuts inter-reader variability by a factor of three.
The practical problem Raidium is solving is tedious and consequential. Oncology radiologists manually track lesions across sequential scans, pulling measurements from prior studies and comparing them to new imaging. This workflow is time-consuming and inconsistent between readers. Raidium Read automates it: the system scans large-volume imaging inputs, detects and segments lesions across anatomical regions, and maps historical lesion data against new follow-up scans. Corti’s Symphony AI took a similar approach to medical coding, treating an error-prone clinical task as a reasoning problem rather than a labelling one.
