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MBZUAI
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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
| 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 | 86.9 / 80.0 | 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 | 64.6 | 72.3 / 64.7 | 70.6 / 70.0 | 56.3 | 59.4 | 84.8 | 26.2 | 63.2 |
| MedMO-4B-Next | 58.7 | 79.7 / 59.6 | 78.0 / 74.0 | 73.3 | 75.7 | 90.6 | 27.0 | 68.5 |
| MedMO-8B-Next | 69.3 | 86.4 / 68.0 | 83.0 / 81.6 | 56.3 | 74.1 | 93.3 | 42.9 | 72.7 |
| 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 | 78.0 | 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 | 19.9 | 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 | 80.2 | 75.6 | 62.0 | 83.8 | 65.2 / 57.8 | 20.9 | 35.5 | 60.1 |
Bold = best result, underline = second-best result.
- Benchmarked on AMD MI210 GPU.
| 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 |
bash
pip install transformers torch qwen-vl-utilspython
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch # Load modelmodel = 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 inputmessages = [ { "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 generatetext = 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])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>"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 | Parameters | Best For |
|---|---|---|
| MedMO-8B-Next | 8B | SOTA highest accuracy, all tasks — recommended |
| MedMO-4B-Next | 4B | 2nd SOTA, high accuracy in resource-constrained environments |
| MedMO-8B | 8B | Previous generation |
| MedMO-4B | 4B | Resource-constrained environments |
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}}This project is licensed under the Apache License 2.0 — see the LICENSE file for details.
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch # Load modelmodel = 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 inputmessages = [ { "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 generatetext = 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])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>"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@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}}