MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images

Multimodal large language models (MLLMs) have rapidly advanced, yet their adoption in medicine remains limited by gaps in domain coverage, modality alignment, and grounded reasoning. In this work, we introduce \textbf{MedMO}, a medical foundation model built upon a generalized MLLM architecture and trained exclusively on large-scale, domain-specific data. MedMO follows a multi-stage training recipe: (i) cross-modal pretraining to align heterogeneous visual encoders with a medical language backbone; (ii) instruction tuning on multi-task supervision that spans captioning, VQA, report generation, retrieval, and grounded disease localization with bounding boxes; and (iii) reinforcement learning with verifiable rewards that combine factuality checks with a box-level GIoU reward to strengthen spatial grounding and step-by-step reasoning in complex clinical scenarios. MedMO consistently outperforms strong open-source medical MLLMs across multiple modalities and tasks. On VQA benchmarks, MedMO achieves an average accuracy improvement of \textbf{+13.8\%} over the baseline and performs within \textbf{1.8\%} of the SOTA Fleming-VL. For text-based QA, it attains \textbf{+7.0\%} over the baseline and \textbf{+14.6\%} over Fleming-VL. In medical report generation, MedMO delivers significant gains in both semantic and clinical accuracy. Moreover, it exhibits strong grounding capability, achieving an IoU improvement of \textbf{+40.4} over the baseline and \textbf{+37.0\%} over Fleming-VL, underscoring its robust spatial reasoning and localization performance.Evaluations across radiology, ophthalmology, pathology, and emergency care confirm MedMO’s broad cross-modality generalization and reliable spatial reasoning. Our code, data, and models will be publicly available.