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

TL;DR

MedMO is a specialized multimodal large language model designed for medical images, trained on domain-specific data, and capable of improved reasoning, grounding, and performance across various medical tasks and modalities.

Contribution

The paper introduces MedMO, a novel medical multimodal foundation model with multi-stage training, domain-specific data, and reinforcement learning for grounded reasoning, surpassing existing medical baselines.

Findings

MedMO-8B-Next improves VQA benchmarks by 6.6% on average.

MedMO enhances medical report generation by 6.7%.

MedMO achieves 56.1 IoU on Bacteria grounding task.

Abstract

Multimodal large language models have advanced rapidly, but their adoption in medicine is constrained by limited domain coverage, imperfect modality alignment, and insufficient grounded reasoning. We introduce MedMO, a medical multimodal foundation model built on a general MLLM architecture and trained exclusively on large-scale domain-specific data. MedMO uses a multi-stage training recipe that includes cross-modal pretraining to align heterogeneous visual encoders with a medical language backbone, instruction tuning with multi-task supervision spanning captioning, VQA, report generation, retrieval, and bounding-box disease localization, and reinforcement learning with verifiable rewards that combine factuality checks with a box-level GIoU signal to improve spatial grounding and step-by-step reasoning in challenging clinical settings. Across modalities and tasks, MedMO surpasses strong open-source medical baselines. MedMO-8B-Next achieves consistent gains on VQA benchmarks, improving by 6.6% on average over Fleming-VL-8B, including gains of 6.0% on MMMU-Med, 9.8% on PMC-VQA, and 21.3% on MedXpertQA. On text-based QA, it improves by 14.4% over Fleming-VL-8B, driven by gains of 8.4% on MMLU-Med and 30.1% on MedQA. For medical report generation, it improves by 6.7% on MIMIC-CXR. MedMO-8B-Next also demonstrates strong grounding performance, reaching 56.1 IoU on Bacteria, which is a 47.8 IoU gain over Fleming-VL-8B. At smaller scale, MedMO-4B-Next remains competitive and exceeds Fleming-VL-8B across VQA, QA, and report generation. Evaluations spanning radiology, ophthalmology, and pathology microscopy further confirm broad cross-modality generalization. Project is available at https://genmilab.github.io/MedMO-Page