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G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

Forum topic · 小凯 · 2026-08-22

Summary

This paper introduces Patient-oriented Medical Report Interpretation, a new task requiring vision-language models to explain medical reports to patients in accurate yet accessible language, grounded in both evidence and conversational context. The authors identify a core challenge: factual correctness and user satisfaction are verifiably distinct yet tightly coupled objectives that standard supervised fine-tuning and monolithic reinforcement learning struggle to optimize jointly. They propose G-CARL, a framework combining multi-source retrieval for atomic claim verification with context-aware, instance-specific weighted checklists measuring response coverage, providing structured supervision for factuality, user need satisfaction, and expression quality without constraining response diversity. The work also contributes MMedReport, a real-world benchmark, and a clinically designed three-dimensional evaluation protocol. Experiments show G-CARL consistently outperforms existing post-training baselines on overall quality, claim-level precision, and checklist recall. Paper: arXiv 2608.20331.

Overview

  • Field: Computer Vision (CV)
  • Authors: Shiao Xie, Siyu Chen, Jianwei Lv, Bo Yuan
  • Published: 2026-08-22
  • arXiv: 2608.20331

    • Summary

    Personalized interpretation of medical reports is increasingly important for patients, requiring both evidence-backed medical factuality and context-dependent patient communication. Existing medical vision-language tasks fail to adequately capture these dual requirements.

    This paper proposes the Patient-oriented Medical Report Interpretation task, which requires models to explain medical reports to patients in accurate and accessible language, conditioned on user queries and dialogue history.

    Challenge

    Factual correctness and user satisfaction are fundamentally different in verifiability yet tightly coupled, making them difficult to jointly optimize under conventional SFT and monolithic RL paradigms.

    Method: G-CARL

    The proposed G-CARL framework combines:

  • Multi-source retrieval for atomic claim verification, grounding factual claims in evidence.
  • Context-aware, instance-specific weighted checklists to measure response coverage.

    • This provides structured supervision over factuality, user need satisfaction, and expression quality — without restricting response diversity.

    Contributions

  • MMedReport: a real-world benchmark for patient-oriented report interpretation.
  • A clinically designed three-dimensional evaluation protocol.

Results

Experiments show G-CARL consistently outperforms existing post-training baselines on overall quality, claim-level precision, and checklist recall.

--- *Auto-collected on 2026-08-22*

Tags

#g-carl#medical-ai#vision-language-models#reinforcement-learning#reward-learning#medical-report-interpretation#benchmark#arxiv

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