@inproceedings{liang-etal-2026-medqpa,
title = "{M}ed{QPA}-Gen: Medical Question Proposing and Answering for Report Generation",
author = "Liang, Weijie and
Zhu, Xiyue and
Zhu, Ruike and
Li, Chenhao and
Tang, Cheng and
Liu, Zhiyu and
Gong, Zhihua and
Luo, Shirui and
Li, Yudu and
Kindratenko, Volodymyr",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2139/",
pages = "43120--43132",
ISBN = "979-8-89176-395-1",
abstract = "Medical report generation from medical images is a vital AI task that helps doctors with diagnosis and marks a significant step toward creating general AI-powered medical systems. However, previous methods either fail to optimize factual accuracy or heavily depend on expert preference data. To overcome these challenges, we propose {MedQPA}, an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. Additionally, we design {MedQPA-Gen}, a medical report generation pipeline that maximizes the {MedQPA} score through prompt engineering and reinforcement learning with {MedQPA} as a reward signal. We demonstrate that {MedQPA} is an accurate evaluation metric that closely correlates with human preferences. More importantly, {MedQPA-Gen} achieves higher human preference scores and better performance on downstream tasks. We open-source code at this repo https://github.com/MedQPA-gen/MedQPA-gen."
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<abstract>Medical report generation from medical images is a vital AI task that helps doctors with diagnosis and marks a significant step toward creating general AI-powered medical systems. However, previous methods either fail to optimize factual accuracy or heavily depend on expert preference data. To overcome these challenges, we propose MedQPA, an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. Additionally, we design MedQPA-Gen, a medical report generation pipeline that maximizes the MedQPA score through prompt engineering and reinforcement learning with MedQPA as a reward signal. We demonstrate that MedQPA is an accurate evaluation metric that closely correlates with human preferences. More importantly, MedQPA-Gen achieves higher human preference scores and better performance on downstream tasks. We open-source code at this repo https://github.com/MedQPA-gen/MedQPA-gen.</abstract>
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%0 Conference Proceedings
%T MedQPA-Gen: Medical Question Proposing and Answering for Report Generation
%A Liang, Weijie
%A Zhu, Xiyue
%A Zhu, Ruike
%A Li, Chenhao
%A Tang, Cheng
%A Liu, Zhiyu
%A Gong, Zhihua
%A Luo, Shirui
%A Li, Yudu
%A Kindratenko, Volodymyr
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liang-etal-2026-medqpa
%X Medical report generation from medical images is a vital AI task that helps doctors with diagnosis and marks a significant step toward creating general AI-powered medical systems. However, previous methods either fail to optimize factual accuracy or heavily depend on expert preference data. To overcome these challenges, we propose MedQPA, an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. Additionally, we design MedQPA-Gen, a medical report generation pipeline that maximizes the MedQPA score through prompt engineering and reinforcement learning with MedQPA as a reward signal. We demonstrate that MedQPA is an accurate evaluation metric that closely correlates with human preferences. More importantly, MedQPA-Gen achieves higher human preference scores and better performance on downstream tasks. We open-source code at this repo https://github.com/MedQPA-gen/MedQPA-gen.
%U https://aclanthology.org/2026.findings-acl.2139/
%P 43120-43132
Markdown (Informal)
[MedQPA-Gen: Medical Question Proposing and Answering for Report Generation](https://aclanthology.org/2026.findings-acl.2139/) (Liang et al., Findings 2026)
ACL
- Weijie Liang, Xiyue Zhu, Ruike Zhu, Chenhao Li, Cheng Tang, Zhiyu Liu, Zhihua Gong, Shirui Luo, Yudu Li, and Volodymyr Kindratenko. 2026. MedQPA-Gen: Medical Question Proposing and Answering for Report Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 43120–43132, San Diego, California, United States. Association for Computational Linguistics.