In this section
@article{Jing:2025:10.1016/j.media.2025.103910,
author = {Jing, P and Lee, K and Zhang, Z and Zhou, H and Yuan, Z and Gao, Z and Zhu, L and Papanastasiou, G and Fang, Y and Yang, G},
doi = {10.1016/j.media.2025.103910},
journal = {Med Image Anal},
title = {Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation.},
url = {http://dx.doi.org/10.1016/j.media.2025.103910},
volume = {109},
year = {2025}
}
TY - JOUR
AB - Radiology report generation is critical for efficiency, but current models often lack the structured reasoning of experts and the ability to explicitly ground findings in anatomical evidence, which limits clinical trust and explainability. This paper introduces BoxMed-RL, a unified training framework to generate spatially verifiable and explainable chest X-ray reports. BoxMed-RL advances chest X-ray report generation through two integrated phases: (1) Pretraining Phase. BoxMed-RL learns radiologist-like reasoning through medical concept learning and enforces spatial grounding with reinforcement learning. (2) Downstream Adapter Phase. Pretrained weights are frozen while a lightweight adapter ensures fluency and clinical credibility. Experiments on two widely used public benchmarks (MIMIC-CXR and IU X-Ray) demonstrate that BoxMed-RL achieves an average 7 % improvement in both METEOR and ROUGE-L metrics compared to state-of-the-art methods. An average 5 % improvement in large language model-based metrics further underscores BoxMed-RL's robustness in generating high-quality reports. Related code and training templates are publicly available at https://github.com/ayanglab/BoxMed-RL.
AU - Jing,P
AU - Lee,K
AU - Zhang,Z
AU - Zhou,H
AU - Yuan,Z
AU - Gao,Z
AU - Zhu,L
AU - Papanastasiou,G
AU - Fang,Y
AU - Yang,G
DO - 10.1016/j.media.2025.103910
PY - 2025///
TI - Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation.
T2 - Med Image Anal
UR - http://dx.doi.org/10.1016/j.media.2025.103910
UR - https://www.ncbi.nlm.nih.gov/pubmed/41412023
VL - 109
ER -
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