Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
Microsoft Mercy Ranjit, Nikhilesh E, Dr. Abhyuday Kumara Swamy, Tanuja Ganu
CARE-X, a chest X-ray vision-language model, was introduced with auxiliary supervision and DAPO reinforcement learning to support calibrated classification, location grounding, and report generation in one system. It reached 94% overall accuracy on the ReXVQA benchmark (41,007 question–answer pairs) as of August 2026. The work changes radiology VLMs by combining structured, tunable confidence outputs and tool-augmented quantitative measurement with generative reporting rather than relying only on free-text predictions.
Why it matters
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research.