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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 in accurate yet accessible language based on user queries and conversation history. The authors identify a key challenge: factual accuracy and patient satisfaction are fundamentally different in verifiability yet tightly coupled, making them hard to jointly optimize under standard SFT or holistic RL paradigms. They propose G-CARL, a framework combining multi-source retrieval for atomic claim verification with context-aware, instance-specific weighted checklists for response coverage. This provides structured supervision for factuality, user need fulfillment, 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 in overall quality, claim-level precision, and checklist recall. Authored by Shiao Xie, Siyu Chen, Jianwei Lv, and Bo Yuan (arXiv:2608.20331, CV field).

Paper 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, demanding both evidence-grounded 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 in accurate and accessible language based on user queries and conversation history.

Key Challenge

Factuality and user satisfaction differ fundamentally in verifiability yet are tightly coupled, making them difficult to jointly optimize under conventional SFT and holistic RL paradigms.

The G-CARL Framework

G-CARL combines:

  • Multi-source retrieval for atomic claim verification (grounding factuality)
  • Context-aware, instance-specific weighted checklists for response coverage

    • This provides structured supervision for factuality, user need fulfillment, and expression quality without constraining response diversity.

    Contributions

  • A new task formulation: Patient-oriented Medical Report Interpretation
  • MMedReport, a real-world benchmark
  • 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

#medical-ai#vision-language-models#reward-learning#reinforcement-learning#medical-report-interpretation#benchmark#arxiv#patient-communication

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