Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911477451980800 |
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| author | Lin, Qika Zhu, Yifan Pu, Bin Huang, Ling Luo, Haoran Ma, Jingying Wu, Feng He, Kai Xu, Jiaxing Peng, Zhen Zhao, Tianzhe Xu, Fangzhi Zhang, Jian Ou, Zhonghong Cambria, Erik Mishra, Swapnil Feng, Mengling |
| author_facet | Lin, Qika Zhu, Yifan Pu, Bin Huang, Ling Luo, Haoran Ma, Jingying Wu, Feng He, Kai Xu, Jiaxing Peng, Zhen Zhao, Tianzhe Xu, Fangzhi Zhang, Jian Ou, Zhonghong Cambria, Erik Mishra, Swapnil Feng, Mengling |
| contents | The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale behind automated decisions. To overcome this fundamental barrier, we introduce DeepMedix-R1, a foundation model (FM) for chest X-ray (CXR) interpretation that generates not only accurate diagnoses but also a transparent, step-by-step reasoning process grounded in specific visual evidence. Our methodology employs a sequential training strategy, beginning with instruction fine-tuning, followed by a cold-start phase to elicit reasoning capabilities. Critically, we then implement reinforcement learning with grounded rewards to meticulously refine the model, aligning both its diagnostic outputs and its reasoning pathways with clinical plausibility. Quantitative assessments show that DeepMedix-R1 substantially outperforms advanced FMs, achieving improvements in report generation and visual question answering tasks. We also introduce Report Arena, a novel LLM-based benchmark that ranks DeepMedix-R1 first among competing models for output quality. Most significantly, a formal review by clinical experts reveals a profound preference for DeepMedix-R1's generated reasoning over the broadly adopted Qwen2.5-VL-7B model, confirming its superior interpretability and clinical utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning Lin, Qika Zhu, Yifan Pu, Bin Huang, Ling Luo, Haoran Ma, Jingying Wu, Feng He, Kai Xu, Jiaxing Peng, Zhen Zhao, Tianzhe Xu, Fangzhi Zhang, Jian Ou, Zhonghong Cambria, Erik Mishra, Swapnil Feng, Mengling Artificial Intelligence The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale behind automated decisions. To overcome this fundamental barrier, we introduce DeepMedix-R1, a foundation model (FM) for chest X-ray (CXR) interpretation that generates not only accurate diagnoses but also a transparent, step-by-step reasoning process grounded in specific visual evidence. Our methodology employs a sequential training strategy, beginning with instruction fine-tuning, followed by a cold-start phase to elicit reasoning capabilities. Critically, we then implement reinforcement learning with grounded rewards to meticulously refine the model, aligning both its diagnostic outputs and its reasoning pathways with clinical plausibility. Quantitative assessments show that DeepMedix-R1 substantially outperforms advanced FMs, achieving improvements in report generation and visual question answering tasks. We also introduce Report Arena, a novel LLM-based benchmark that ranks DeepMedix-R1 first among competing models for output quality. Most significantly, a formal review by clinical experts reveals a profound preference for DeepMedix-R1's generated reasoning over the broadly adopted Qwen2.5-VL-7B model, confirming its superior interpretability and clinical utility. |
| title | Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03906 |