CARE: Confidence-Aware Reasoning for Reliable Medical VQA
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
Record details
Published: 11 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Healthcare
Retrieved: 12 August 2026
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ethics.ai (11 August 2026), “CARE: Confidence-Aware Reasoning for Reliable Medical VQA,” evidence record 18657, https://ethics.ai/record/18657 (originally published by arXiv cs.AI).
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