Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG), a novel vision-language task that demands clinical reasoning and pixel-level grounding. Second, we release U-MRG-14K, a dataset of 14K samples featuring pixel-level masks alongside implicit clinical queries and reasoning traces, spanning 10 modalities, 15 super-categories, and 108 specific categories. Finally, we introduce MedReasoner, a modular framework that distinctly separates reasoning from segmentation: an MLLM reasoner is optimized with reinforcement learning, while a frozen segmentation expert converts spatial prompts into masks, with alignment achieved through format and accuracy rewards. MedReasoner achieves state-of-the-art performance on U-MRG-14K and demonstrates strong generalization to unseen clinical queries, underscoring the significant promise of reinforcement learning for interpretable medical grounding.

翻译：在医学影像中，准确定位感兴趣区域对于诊断和治疗规划至关重要。尽管多模态大语言模型结合了视觉感知与自然语言，但当前的医学定位流程仍依赖于带有显式空间提示的监督微调，使其难以处理临床实践中常见的隐式查询。本研究做出了三项核心贡献。首先，我们定义了统一医学推理定位，这是一项需要临床推理和像素级定位的新型视觉-语言任务。其次，我们发布了U-MRG-14K数据集，包含14K个样本，每个样本均配有像素级掩码、隐式临床查询及推理轨迹，涵盖10种模态、15个超类别和108个具体类别。最后，我们提出了MedReasoner，一个将推理与分割明确分离的模块化框架：通过强化学习优化MLLM推理器，而冻结的分割专家将空间提示转换为掩码，并通过格式和准确性奖励实现对齐。MedReasoner在U-MRG-14K上取得了最先进的性能，并对未见过的临床查询展现出强大的泛化能力，这凸显了强化学习在可解释医学定位中的巨大潜力。