Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

The CARE-X model has been launched as a research initiative aimed at enhancing X-ray interpretation. This model combines report generation with structured predictions, providing both accurate outputs and free-text reasoning. CARE-X employs reinforcement learning to ensure clinical correctness across various tasks. As a unified model for chest X-ray interpretation, CARE-X enables healthcare professionals to tackle a wide array of tasks, such as accurately identifying abnormalities and generating detailed reports. The model was validated using real-world clinical data from Narayana Health in India, showcasing its effectiveness in rare cases.