Current AI solutions are narrow in scope, tackling isolated tasks (e.g., organ segmentation) rather than providing an end-to-end, AI-driven diagnostic workflow. Radiologists waste time toggling between fragmented systems, reducing efficiency. Current progress in machine learning research, in particular foundation models, offers new perspectives. This talk will address how the seamless measure of context aware, advanced biomarkers, as well as a new multi-modal interface for radiologists, holds the promise of bridging research and care and make precision radiology a reality for patients.

Organized in collaboration with Business France