Curia: A Multi-Modal Foundation Model for Radiology
Authors: name: Corentin Dancette, name: Julien Khlaut, name: Antoine Saporta, name: Helene Philippe, name: Elodie Ferreres, name: Baptiste Callard, name: Théo Danielou, name: Léo Alberge, name: Léo Machado, name: Daniel Tordjman, name: Julie Dupuis, name: Korentin Le Floch, name: Jean Du Terrail, name: Mariam Moshiri, name: Laurent Dercle, name: Tom Boeken, name: Jules Gregory, name: Maxime Ronot, name: François Legou, name: Pascal Roux, name: Marc Sapoval, name: Pierre Manceron, name: Paul Hérent

AI-assisted radiological interpretation is based on predominantly narrow,
single-task models. This approach is impractical for covering the vast spectrum
of imaging modalities, diseases, and radiological findings. Foundation models
(FMs) hold the promise of broad generalization across modalities and in
low-data settings. However, this potential has remained largely unrealized in
radiology. We introduce Curia, a foundation model trained on the entire
cross-sectional imaging output of a major hospital over several years, which to
our knowledge is the largest such corpus of real-world data-encompassing
150,000 exams (130 TB). On a newly curated 19-task external validation
benchmark, Curia accurately identifies organs, detects conditions like brain
hemorrhages and myocardial infarctions, and predicts outcomes in tumor staging.
Curia meets or surpasses the performance of radiologists and recent foundation
models, and exhibits clinically significant emergent properties in
cross-modality, and low-data regimes. To accelerate progress, we release our
base model's weights at https://huggingface.co/raidium/curia.