SourceCare-X makes radiology AI more helpful in the clinic
Combining text generation with precise measurement tools could help clinicians identify borderline conditions like aortic dilation often missed during initial screenings, potentially improving cardiovascular care outcomes.
- CARE-X is built on SigLIP2-so400M vision encoder and Phi-4-mini-instruct language backbone.
- Evaluated on real-world Indian clinical data from Narayana Health across 1,047 de-identified chest radiographs.
- Achieved 94% accuracy on the ReXVQA benchmark and was selected as a finalist at the IHF Innovation Hub, World Hospital Congress 2026.