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CARE: Confidence-Aware Reasoning for Reliable Medical VQA

Research Medical/Healthcare AI

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Content 80
Popularity 33

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Merged summary

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.

Sources (1)

CARE: Confidence-Aware Reasoning for Reliable Medical VQA

arXiv cs.CV Yuetian Du, Yucheng Wang, Zhenyuan Chen, Luyuan Chen, Rongyu Zhang, Jinjian Zhang, Wei Zhou, Zhijie Xu, Ming Kong, Zhan Zhou, Jie Liu, Qiang Zhu 2026-08-11 arXiv:2608.10964
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-24 14:22:47.745747 UTC

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.
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