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lingshu-medical-mllm
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Lingshu-7B

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qwen2_5_vl
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clinical-reasoning
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Website    🤖 7B Model    🤖 32B Model    MedEvalKit    Technical Report

Lingshu - SOTA Multimodal Large Language Models for Medical Domain

BIG NEWS: Lingshu is released with state-of-the-art performance on medical VQA tasks and report generation.

This repository contains the model of the paper Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. We also release a comprehensive medical evaluation toolkit in MedEvalKit, which supports fast evaluation of major multimodal and textual medical tasks.

Highlights

  • Lingshu models achieve SOTA on most medical multimodal/textual QA and report generation tasks for 7B and 32 model sizes.
  • Lingshu-32B outperforms GPT-4.1 and Claude Sonnet 4 in most multimodal QA and report generation tasks.
  • Lingshu supports more than 12 medical imaging modalities, including X-Ray, CT Scan, MRI, Microscopy, Ultrasound, Histopathology, Dermoscopy, Fundus, OCT, Digital Photography, Endoscopy, and PET.

Release

Disclaimer: We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.

Evaluation

Medical Multimodal VQA

Models MMMU-Med VQA-RAD SLAKE PathVQA PMC-VQA OmniMedVQA MedXpertQA Avg.
Proprietary Models
GPT-4.1 75.2 65.0 72.2 55.5 55.2 75.5 45.2 63.4
Claude Sonnet 4 74.6 67.6 70.6 54.2 54.4 65.5 43.3 61.5
Gemini-2.5-Flash 76.9 68.5 75.8 55.4 55.4 71.0 52.8 65.1
Open-source Models (<10B)
BiomedGPT 24.9 16.6 13.6 11.3 27.6 27.9 - -
Med-R1-2B 34.8 39.0 54.5 15.3 47.4 - 21.1 -
MedVLM-R1-2B 35.2 48.6 56.0 32.5 47.6 77.7 20.4 45.4
MedGemma-4B-IT 43.7 72.5 76.4 48.8 49.9 69.8 22.3 54.8
LLaVA-Med-7B 29.3 53.7 48.0 38.8 30.5 44.3 20.3 37.8
HuatuoGPT-V-7B 47.3 67.0 67.8 48.0 53.3 74.2 21.6 54.2
BioMediX2-8B 39.8 49.2 57.7 37.0 43.5 63.3 21.8 44.6
Qwen2.5VL-7B 50.6 64.5 67.2 44.1 51.9 63.6 22.3 52.0
InternVL2.5-8B 53.5 59.4 69.0 42.1 51.3 81.3 21.7 54.0
InternVL3-8B 59.2 65.4 72.8 48.6 53.8 79.1 22.4 57.3
Lingshu-7B 54.0 67.9 83.1 61.9 56.3 82.9 26.7 61.8
Open-source Models (>10B)
HealthGPT-14B 49.6 65.0 66.1 56.7 56.4 75.2 24.7 56.2
HuatuoGPT-V-34B 51.8 61.4 69.5 44.4 56.6 74.0 22.1 54.3
MedDr-40B 49.3 65.2 66.4 53.5 13.9 64.3 - -
InternVL3-14B 63.1 66.3 72.8 48.0 54.1 78.9 23.1 58.0
Qwen2.5V-32B 59.6 71.8 71.2 41.9 54.5 68.2 25.2 56.1
InternVL2.5-38B 61.6 61.4 70.3 46.9 57.2 79.9 24.4 57.4
InternVL3-38B 65.2 65.4 72.7 51.0 56.6 79.8 25.2 59.4
Lingshu-32B 62.3 76.5 89.2 65.9 57.9 83.4 30.9 66.6

Medical Textual QA

Models MMLU-Med PubMedQA MedMCQA MedQA Medbullets MedXpertQA SuperGPQA-Med Avg.
Proprietary Models
GPT-4.1 89.6 75.6 77.7 89.1 77.0 30.9 49.9 70.0
Claude Sonnet 4 91.3 78.6 79.3 92.1 80.2 33.6 56.3 73.1
Gemini-2.5-Flash 84.2 73.8 73.6 91.2 77.6 35.6 53.3 69.9
Open-source Models (<10B)
Med-R1-2B 51.5 66.2 39.1 39.9 33.6 11.2 17.9 37.0
MedVLM-R1-2B 51.8 66.4 39.7 42.3 33.8 11.8 19.1 37.8
MedGemma-4B-IT 66.7 72.2 52.2 56.2 45.6 12.8 21.6 46.8
LLaVA-Med-7B 50.6 26.4 39.4 42.0 34.4 9.9 16.1 31.3
HuatuoGPT-V-7B 69.3 72.8 51.2 52.9 40.9 10.1 21.9 45.6
BioMediX2-8B 68.6 75.2 52.9 58.9 45.9 13.4 25.2 48.6
Qwen2.5VL-7B 73.4 76.4 52.6 57.3 42.1 12.8 26.3 48.7
InternVL2.5-8B 74.2 76.4 52.4 53.7 42.4 11.6 26.1 48.1
InternVL3-8B 77.5 75.4 57.7 62.1 48.5 13.1 31.2 52.2
Lingshu-7B 74.5 76.6 55.9 63.3 56.2 16.5 26.3 52.8
Open-source Models (>10B)
HealthGPT-14B 80.2 68.0 63.4 66.2 39.8 11.3 25.7 50.7
HuatuoGPT-V-34B 74.7 72.2 54.7 58.8 42.7 11.4 26.5 48.7
MedDr-40B 65.2 77.4 38.4 59.2 44.3 12.0 24.0 45.8
InternVL3-14B 81.7 77.2 62.0 70.1 49.5 14.1 37.9 56.1
Qwen2.5VL-32B 83.2 68.4 63.0 71.6 54.2 15.6 37.6 56.2
InternVL2.5-38B 84.6 74.2 65.9 74.4 55.0 14.7 39.9 58.4
InternVL3-38B 83.8 73.2 64.9 73.5 54.6 16.0 42.5 58.4
Lingshu-32B 84.7 77.8 66.1 74.7 65.4 22.7 41.1 61.8

Medical Report Generation

Models MIMIC-CXR CheXpert Plus IU-Xray
ROUGE-L CIDEr RaTE SembScore RadCliQ-v1-1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1-1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1-1
Proprietary Models
GPT-4.1 9.0 82.8 51.3 23.9 57.1 24.5 78.8 45.5 23.2 45.5 30.2 124.6 51.3 47.5 80.3
Claude Sonnet 4 20.0 56.6 45.6 19.7 53.4 22.0 59.5 43.5 18.9 43.3 25.4 88.3 55.4 41.0 72.1
Gemini-2.5-Flash 25.4 80.7 50.3 29.7 59.4 23.6 72.2 44.3 27.4 44.0 33.5 129.3 55.6 50.9 91.6
Open-source Models (<10B)
Med-R1-2B 19.3 35.4 40.6 14.8 42.4 18.6 37.1 38.5 17.8 37.6 16.1 38.3 41.4 12.5 43.6
MedVLM-R1-2B 20.3 40.1 41.6 14.2 48.3 20.9 43.5 38.9 15.5 40.9 22.7 61.1 46.1 22.7 54.3
MedGemma-4B-IT 25.6 81.0 52.4 29.2 62.9 27.1 79.0 47.2 29.3 46.6 30.8 103.6 57.0 46.8 86.7
LLaVA-Med-7B 15.0 43.4 12.8 18.3 52.9 18.4 45.5 38.8 23.5 44.0 18.8 68.2 40.9 16.0 58.1
HuatuoGPT-V-7B 23.4 69.5 48.9 20.0 48.2 21.3 64.7 44.2 19.3 39.4 29.6 104.3 52.9 40.7 63.6
BioMediX2-8B 20.0 52.8 44.4 17.7 53.0 18.1 47.9 40.8 21.6 43.3 19.6 58.8 40.1 11.6 53.8
Qwen2.5VL-7B 24.1 63.7 47.0 18.4 55.1 22.2 62.0 41.0 17.2 43.1 26.5 78.1 48.4 36.3 66.1
InternVL2.5-8B 23.2 61.8 47.0 21.0 56.2 20.6 58.5 43.1 19.7 42.7 24.8 75.4 51.1 36.7 67.0
InternVL3-8B 22.9 66.2 48.2 21.5 55.1 20.9 65.4 44.3 25.2 43.7 22.9 76.2 51.2 31.3 59.9
Lingshu-7B 30.8 109.4 52.1 30.0 69.2 26.5 79.0 45.4 26.8 47.3 41.2 180.7 57.6 48.4 108.1
Open-source Models (>10B)
HealthGPT-14B 21.4 64.7 48.4 16.5 52.7 20.6 66.2 44.4 22.7 42.6 22.9 81.9 50.8 16.6 56.9
HuatuoGPT-V-34B 23.5 68.5 48.5 23.0 47.1 22.5 62.8 42.9 22.1 39.7 28.2 108.3 54.4 42.2 59.3
MedDr-40B 15.7 62.3 45.2 12.2 47.0 24.1 66.1 44.7 24.2 44.7 19.4 62.9 40.3 7.3 48.9
InternVL3-14B 22.0 63.7 48.6 17.4 46.5 20.4 60.2 44.1 20.7 39.4 24.8 93.7 55.0 38.7 55.0
Qwen2.5VL-32B 15.7 50.2 47.5 17.1 45.2 15.2 54.8 43.4 18.5 40.3 18.9 73.3 51.3 38.1 54.0
InternVL2.5-38B 22.7 61.4 47.5 18.2 54.9 21.6 60.6 42.6 20.3 45.4 28.9 96.5 53.5 38.5 69.7
InternVL3-38B 22.8 64.6 47.9 18.1 47.2 20.5 62.7 43.8 20.2 39.4 25.5 90.7 53.5 33.1 55.2
Lingshu-32B 28.8 96.4 50.8 30.1 67.1 25.3 75.9 43.4 24.2 47.1 42.8 189.2 63.5 54.6 130.4

Usage

Using transformers 

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info


# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "lingshu-medical-mllm/Lingshu-7B",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("lingshu-medical-mllm/Lingshu-7B")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "example.png",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
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",
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
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)

Using vLLM 

from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info
import PIL
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("lingshu-medical-mllm/Lingshu-7B")
llm = LLM(model="lingshu-medical-mllm/Lingshu-7B", limit_mm_per_prompt = {"image": 4}, tensor_parallel_size=2, enforce_eager=True, trust_remote_code=True,)
sampling_params = SamplingParams(
            temperature=0.7,
            top_p=1,
            repetition_penalty=1,
            max_tokens=1024,
            stop_token_ids=[],
        )

text = "What does the image show?"
image_path = "example.png"
image = PIL.Image.open(image_path)

message = [
    {
        "role":"user",
        "content":[
            {"type":"image","image":image},
            {"type":"text","text":text}
            ]
            }
]
prompt = processor.apply_chat_template(
    message,
    tokenize=False,
    add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(message)
mm_data = {}
mm_data["image"] = image_inputs
processed_input = {
  "prompt": prompt,
  "multi_modal_data": mm_data,
}

outputs = llm.generate([processed_input], sampling_params=sampling_params)
print(outputs[0].outputs[0].text)

Citation

If you find our project useful, we hope you would kindly star our repo and cite our work as follows:

  • * are equal contributions. ^ are corresponding authors.
@misc{lasateam2025lingshu,
      title={Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning}, 
      author={LASA Team and Weiwen Xu* and Hou Pong Chan* and Long Li* and Mahani Aljunied and Ruifeng Yuan and Jianyu Wang and Chenghao Xiao and Guizhen Chen and Chaoqun Liu and Zhaodonghui Li and Yu Sun and Junao Shen and Chaojun Wang and Jie Tan and Deli Zhao and Tingyang Xu and Hao Zhang^ and Yu Rong^},
      year={2025},
      eprint={2506.07044},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.07044}, 
}
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