CARE: Confidence-Aware Reasoning for Reliable Medical VQA

TL;DR - CARE is a dual-stage fine-tuning framework that makes medical multimodal LLMs express calibrated confidence alongside chain-of-thought answers for visual question answering, addressing the gap between stated certainty and real diagnostic accuracy that erodes clinical trust.

• Stage 1 uses a scalable Medical-CoT synthesis pipeline to generate structured cold-start reasoning data for supervised fine-tuning.
• Stage 2 applies GRPO reinforcement learning with a Confidence-Aware Reward (CAR) that couples expressed confidence to diagnostic correctness in the reward signal.
• Reported results across three Medical VQA benchmarks: highest diagnostic accuracy plus lowest Expected Calibration Error and hallucination rate; code released on GitHub.
• Framing targets trustworthy clinical decision support, treating calibration as a first-class objective rather than a post-hoc correction.