Scaling medical imaging report generation with multimodal reinforcement learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Liu, Qianchu Zhang, Sheng Qin, Guanghui Gu, Yu Jin, Ying Preston, Sam Xu, Yanbo Kiblawi, Sid Yim, Wen-wai Ossowski, Tim Naumann, Tristan Wei, Mu Poon, Hoifung |
| author_facet | Liu, Qianchu Zhang, Sheng Qin, Guanghui Gu, Yu Jin, Ying Preston, Sam Xu, Yanbo Kiblawi, Sid Yim, Wen-wai Ossowski, Tim Naumann, Tristan Wei, Mu Poon, Hoifung |
| contents | Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal understanding and reasoning especially in high-value verticals such as biomedicine. Medical imaging report generation is a prominent example. Supervised fine-tuning can substantially improve performance, but they are prone to overfitting to superficial boilerplate patterns. In this paper, we introduce Universal Report Generation (UniRG) as a general framework for medical imaging report generation. By leveraging reinforcement learning as a unifying mechanism to directly optimize for evaluation metrics designed for end applications, UniRG can significantly improve upon supervised fine-tuning and attain durable generalization across diverse institutions and clinical practices. We trained UniRG-CXR on publicly available chest X-ray (CXR) data and conducted a thorough evaluation in CXR report generation with rigorous evaluation scenarios. On the authoritative ReXrank benchmark, UniRG-CXR sets new overall SOTA, outperforming prior state of the art by a wide margin. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17151 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scaling medical imaging report generation with multimodal reinforcement learning Liu, Qianchu Zhang, Sheng Qin, Guanghui Gu, Yu Jin, Ying Preston, Sam Xu, Yanbo Kiblawi, Sid Yim, Wen-wai Ossowski, Tim Naumann, Tristan Wei, Mu Poon, Hoifung Computer Vision and Pattern Recognition Computation and Language Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal understanding and reasoning especially in high-value verticals such as biomedicine. Medical imaging report generation is a prominent example. Supervised fine-tuning can substantially improve performance, but they are prone to overfitting to superficial boilerplate patterns. In this paper, we introduce Universal Report Generation (UniRG) as a general framework for medical imaging report generation. By leveraging reinforcement learning as a unifying mechanism to directly optimize for evaluation metrics designed for end applications, UniRG can significantly improve upon supervised fine-tuning and attain durable generalization across diverse institutions and clinical practices. We trained UniRG-CXR on publicly available chest X-ray (CXR) data and conducted a thorough evaluation in CXR report generation with rigorous evaluation scenarios. On the authoritative ReXrank benchmark, UniRG-CXR sets new overall SOTA, outperforming prior state of the art by a wide margin. |
| title | Scaling medical imaging report generation with multimodal reinforcement learning |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2601.17151 |