Paper Overview
- Field: NLP
- Authors: Shiao Xie, Siyu Chen, Jianwei Lv, Bo Yuan, Yujin Wang, Xiandong Li
- Published: 2026-08-22
- arXiv: 2608.20331
- New task (PMRI): open-ended, patient-facing interpretation of medical reports combining factual grounding with context-aware communication.
- Multimodal dataset: 3,020 radiology reports paired with patient background information and corresponding medical images.
- G-CARL (Grounded Checklist-Aligned Reward Learning): an RLHF method that uses structured checklists to guide models, ensuring outputs are factually accurate and tailored to individual patients.
- Paper: https://arxiv.org/abs/2608.20331
Summary
Personalized interpretation of medical reports has become an increasingly important need among patients. Meeting this need requires both evidence-grounded medical factuality and context-dependent patient communication, yet existing medical vision-language tasks fail to fully capture these dual requirements.
To bridge this gap, the authors introduce Patient-Oriented Medical Report Interpretation (PMRI), a novel open-ended multimodal generation task in which models must explain medical report findings in plain language while remaining grounded in visual evidence and adapted to each patient's specific background.
Contributions
Results
Experiments show that G-CARL significantly improves both factual accuracy and patient satisfaction, paving the way for more personalized and reliable communication in medical AI.