让医疗视觉问答模型说真话，准确又知错。
CARE: Confidence-Aware Reasoning for Reliable Medical VQA
- 用结构化数据+双阶段优化，让模型回答时既准又知深浅。
- 在三个医疗问答数据集上，准确率最高，错误率最低。
- 适合临床决策支持系统，提升医生对AI的信任度。
强化学习微调使医疗多模态大模型能生成思维链推理，但其存在信心校准偏差——表达的确定性与实际诊断准确性之间存在系统性差距，削弱临床可信度。本文提出CARE（自信感知医疗推理框架），通过双阶段流程联合优化准确率与校准度。首先，采用可扩展的医学思维链合成方法生成结构化冷启动数据用于监督微调；其次，引入新型自信感知奖励机制（CAR）的组相对策略优化（GRPO），将模型信心与诊断正确性关联至奖励信号。在三个医疗视觉问答基准测试中，CARE达到最高诊断准确率，同时实现最低期望校准误差和幻觉率，为可信临床决策支持奠定基础。代码已开源。
原文摘要 · Abstract (English)
Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual question answering, yet these models suffer from $\textit{confidence miscalibration}$---a systematic gap between expressed certainty and actual diagnostic accuracy that undermines clinical trust. We propose $\textbf{CARE}$, a $\textbf{C}$onfidence-$\textbf{A}$ware medical $\textbf{RE}$asoning framework that jointly optimizes accuracy and calibration through a dual-stage pipeline. First, a scalable Medical-CoT synthesis provides structured cold-start data for Supervised Fine-Tuning. Second, Group Relative Policy Optimization (GRPO) with a novel $\textbf{Confidence-Aware Reward (CAR)}$ mechanism ties the model's confidence to diagnostic correctness within the reward signal. Across three Medical VQA benchmarks, $\textbf{CARE}$ achieves the highest diagnostic accuracy while obtaining the lowest Expected Calibration Error and Hallucination Rate, establishing a foundation for trustworthy clinical decision support. Our code is available at https://github.com/anotherbricki/CARE.
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