README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - medical
  - multimodal
  - grounding
  - report-generation
  - radiology
  - clinical-reasoning
  - mri
  - ct
  - histopathology
  - x-ray
  - fundus
---


# MedMO-8B-Next: Grounding and Understanding Multimodal Large Language Model for Medical Images

[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2602.06965)
[![Model](https://img.shields.io/badge/🤗-MedMO--4B-blue)](https://huggingface.co/MBZUAI/MedMO-4B)
[![Model](https://img.shields.io/badge/🤗-MedMO--4B--Next-blue)](https://huggingface.co/MBZUAI/MedMO-4B-Next)
[![Model](https://img.shields.io/badge/🤗-MedMO--8B-blue)](https://huggingface.co/MBZUAI/MedMO-8B)
[![Model](https://img.shields.io/badge/🤗-MedMO--8B--Next-blue)](https://huggingface.co/MBZUAI/MedMO-8B-Next)
[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

<p align="center">
  <img src="MedMO-logo.png" alt="MedMO Logo" width="300"/>
</p>

**MedMO-8B-Next** is the latest and most powerful iteration of the MedMO family — an open-source multimodal foundation model purpose-built for comprehensive medical image understanding and grounding. Trained on **26M+ diverse medical samples across 45 datasets**, MedMO-8B-Next achieves **state-of-the-art performance across all major medical imaging benchmarks**, outperforming both open-source and closed-source competitors on VQA, Text QA, grounding, and report generation tasks.

---

## 🏆 Benchmark Performance

### VQA & Text QA Results

MedMO-8B-Next sets a new state-of-the-art across the board, achieving the highest average scores on both medical VQA and Text QA benchmarks — surpassing strong baselines including Lingshu-7B and Fleming-VL-8B.

> OMIVQA = OmniMedVQA · MedXQA = MedXpertQA · Medbullets reported as op4/op5

#### Medical VQA Benchmarks

| Model | MMMU-Med | VQA-RAD (closed/all) | SLAKE (closed/all) | PathVQA | PMC-VQA | OmniMedVQA | MedXpertQA | **Avg.** |
|---|---|---|---|---|---|---|---|---|
| Lingshu-7B | 54.0 | 77.2 / 43.0 | 82.4 / 33.2 | 41.9 | 54.2 | 82.9 | 26.9 | 55.1 |
| Fleming-VL-8B | 63.3 | 78.4 / 56.4 | <u>86.9 / 80.0</u> | 56.5 | 64.3 | 88.2 | 21.6 | 66.1 |
| MediX-R1-8B | 63.3 | 75.2/51.6 | 70.3/54.4 | 41.0 | 55.3 | 73.8 | 24.9 | 57.1 |
| MedMO-4B | 54.6 | 50.9 / 35.0 | 41.0 / 30.0 | 42.4 | 50.6 | 79.7 | 24.8 | 45.4 |
| MedMO-8B | <u>64.6</u> | 72.3 / 64.7 | 70.6 / 70.0 | 56.3 | 59.4 | 84.8 | 26.2 | 63.2 |
| MedMO-4B-Next | 58.7 | <u>79.7 / 59.6</u> | 78.0 / 74.0 | **73.3** | **75.7** | <u>90.6</u> | <u>27.0</u> | <u>68.5</u> |
| **MedMO-8B-Next** | **69.3** | **86.4 / 68.0** | **83.0 / 81.6** | <u>56.3</u> | <u>74.1</u> | **93.3** | **42.9** | **72.7** |

#### Medical Text QA Benchmarks

| Model | MMLU-Med | PubMedQA | MedMCQA | MedQA | Medbullets (op4/op5) | MedXpertQA | SGPQA | **Avg.** |
|---|---|---|---|---|---|---|---|---|
| Lingshu-7B | 69.6 | 75.8 | 56.3 | 63.5 | 62.0 / 53.8 | 16.4 | 27.5 | 53.1 |
| Fleming-VL-8B | 71.8 | 74.0 | 51.8 | 53.7 | 40.5 / 37.3 | 12.1 | 24.9 | 45.7 |
| MediX-R1-8B | 79.0 | 73.4 | 60.1 | 85.8 | 55.1/47.0 | 14.4 | 34.3 | 56.1 |
| MedMO-4B | 75.7 | <u>78.0</u> | 58.0 | 78.5 | 57.5 / 47.7 | 16.4 | 29.4 | 55.1 |
| MedMO-8B | **81.0** | 77.6 | **65.0** | **84.3** | **66.5 / 60.2** | <u>19.9</u> | **36.0** | **61.3** |
| MedMO-4B-Next | 74.8 | **78.2** | 58.1 | 78.3 | 57.4 / 47.6 | 16.5 | 29.5 | 55.0 |
| **MedMO-8B-Next** | <u>80.2</u> | 75.6 | <u>62.0</u> | <u>83.8</u> | <u>65.2 / 57.8</u> | **20.9** | <u>35.5</u> | <u>60.1</u> |

> **Bold** = best result, <u>underline</u> = second-best result.
> * Benchmarked on AMD MI210 GPU.

---

### Supported Imaging Modalities

| Domain | Modalities |
|---|---|
| Radiology | X-ray, CT, MRI, Ultrasound |
| Pathology | Whole-slide imaging, Microscopy |
| Ophthalmology | Fundus photography, OCT |
| Dermatology | Clinical skin images |
| Nuclear Medicine | PET, SPECT |

---

## 🚀 Quick Start

### Installation

```bash
pip install transformers torch qwen-vl-utils
```

### Basic Usage

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "MBZUAI/MedMO-8B-Next",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("MBZUAI/MedMO-8B-Next")

# Prepare input
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/medical/image.png",
            },
            {"type": "text", "text": "What abnormalities are present in this chest X-ray?"},
        ],
    }
]

# Process and generate
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```

### Example: Disease Localization with Bounding Boxes

```python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "chest_xray.png"},
            {"type": "text", "text": "Detect and localize all abnormalities in this image."},
        ],
    }
]
# Example output:
# "Fractures <box>[[156, 516, 231, 607], [240, 529, 296, 581]]</box>"
```

### Example: Radiology Report Generation

```python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "ct_scan.png"},
            {"type": "text", "text": "Generate a detailed radiology report for this CT scan."},
        ],
    }
]
# MedMO-8B-Next generates comprehensive clinical reports with findings and impressions
```



---

## 📦 Model Family

| Model | Parameters | Best For |
|---|---|---|
| [MedMO-8B-Next](https://huggingface.co/MBZUAI/MedMO-8B-Next) | 8B | SOTA highest accuracy, all tasks — **recommended** |
| [MedMO-4B-Next](https://huggingface.co/MBZUAI/MedMO-4B-Next) | 4B | 2nd SOTA, high accuracy in resource-constrained environments |
| [MedMO-8B](https://huggingface.co/MBZUAI/MedMO-8B) | 8B | Previous generation |
| [MedMO-4B](https://huggingface.co/MBZUAI/MedMO-4B) | 4B | Resource-constrained environments |

---

## 📄 Citation

If you use MedMO in your research, please cite our paper:

```bibtex
@article{deria2026medmo,
  title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images},
  author={Deria, Ankan and Kumar, Komal and Dukre, Adinath Madhavrao and Segal, Eran and Khan, Salman and Razzak, Imran},
  journal={arXiv preprint arXiv:2602.06965},
  year={2026}
}
```

---

## 📜 License

This project is licensed under the **Apache License 2.0** — see the [LICENSE](LICENSE) file for details.