MedMO

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

Medical Text QA Benchmarks

Bold = best result, underline = second-best result.

• Benchmarked on AMD MI210 GPU.

Supported Imaging Modalities

🚀 Quick Start

Installation

pip install transformers torch qwen-vl-utils

Basic Usage

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

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

Example: Radiology Report Generation

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

📄 Citation

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

@article{deria2026medmo,
  title={MedMO: Grounding and Understanding Multimodal Large Lan