madrimed1.2-VL-2B

Overview

MadriMed 1.2 is the second compact multimodal medical vision-language model by Madrisight. Building upon madriMed-VL-2B, MadriMed 1.2 delivers significant performance leaps across medical image understanding (Radiology, Pathology, CT, MRI, X-Ray) and medical reasoning.

It Delivers robust clinical reasoning capabilities, drastically reducing hallucinations and diagnostic contradictions while setting a new state-of-the-art benchmark for 2B medical vision models.

What's New in MadriMed 1.2?

1. Massive Diagnostic Accuracy Gains:
   • Massive jump on VQA-RAD overall score and most of the other evalution benchmarks.
2. Resistant to Generation Hallucinations:
   • Substantially reduced token corruption, repetition and positive-bias tendencies observed in version one.
3. Structured Formating:
   • Trained with graduated correctness scoring and reasoning-integrity penalties to enforce structured.

Quick Start

Installation

pip install torch torchvision transformers

Input

prompt = """Question: Examine the mammogram image shown above. Which of the following findings is most evident?

Answer Choices:
A. Well-circumscribed round mass with benign features
B. Clustered microcalcifications within an area of irregular density
C. Fat-containing lesion consistent with lipoma
D. Diffuse bilateral breast edema

Follow this exact step-by-step reasoning structure.

### 1. Key Clinical Findings
Extract the patient's age, gender, main symptoms, duration, objective physical exam findings, and abnormal laboratory/imaging results from the provided question and the images.

### 2. Differential Diagnosis
List 2-3 highly probable conditions based on the extracted findings. Briefly state why the current presentation fits.

### 3. Option Elimination 
Analyze each provided choice. Write one sentence explaining why it is either correct or incorrect based on clinical guidelines.

### 4. Conclusion & Final Answer
State the final diagnostic/management conclusion.

### 5. Final correct choice
Provide final single correct choice.
"""

Run the model directly

import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
from PIL import Image

MODEL_ID = "madrisight/madrimed1.2-VL-2B"
device = "mps" if torch.backends.mps.is_available() else "cpu"

model = Qwen3VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    device_map=device,
    dtype=torch.float32,
    trust_remote_code=True,
).eval()

processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

# Multi-image input
image_paths = ["images/MM-1-a.png", "images/MM-1-b.png"]
images = [Image.open(path).convert("RGB") for path in image_paths]

# Chat Message Payload
messages = [
    {
        "role": "system",
        "content": "You are an expert clinical medical educator and USMLE board reviewer.",
    },
    {
        "role": "user",
        "content": [{"type": "image"} for _ in images] + [{"type": "text", "text": prompt}]
    }
]

# Tokenize, Process, and Generate
with torch.inference_mode():
    text = processor.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    inputs = processor(
        text=text,
        images=images,  # Pass the flat list of PIL Image objects
        return_tensors="pt",
        padding=True,
        truncation=True,
    ).to(device)

    generated_ids = model.generate(
        **inputs,
        max_new_tokens=1024,
        do_sample=False,
    )

    output_text = processor.batch_decode(
        generated_ids[:, inputs.input_ids.shape[1]:],
        skip_special_tokens=True
    )[0].strip()

Output

### 1. Key Clinical Findings
- **Patient's age and gender**: Not explicitly mentioned, but mammograms are typically used for women.
- **Main symptoms**: Not specified, but mammograms are often performed for breast abnormalities.
- **Duration**: Not specified, but mammograms are usually performed at the time of suspicion.
- **Objective physical exam findings**: Not specified, but mammograms are used to detect abnormalities.
- **Abnormal laboratory/imaging results**: Not specified, but mammograms are used to detect breast abnormalities.

### 2. Differential Diagnosis
- **Well-circumscribed round mass with benign features**: This could be a benign lesion such as a fibroadenoma.
- **Clustered microcalcifications within an area of irregular density**: This could indicate a malignant process, such as ductal carcinoma in situ (DCIS) or invasive breast cancer.
- **Fat-containing lesion consistent with lipoma**: This is a benign lesion and could be a lipoma.

### 3. Option Elimination
- **Well-circumscribed round mass with benign features**: This is a plausible finding, but it is less likely to be the primary concern in a mammogram without additional symptoms or findings.
- **Clustered microcalcifications within an area of irregular density**: This is a highly suspicious finding for malignancy, as it is often associated with breast cancer.
- **Fat-containing lesion consistent with lipoma**: This is a benign finding and less likely to be the primary concern in a mammogram without additional symptoms or findings.

### 4. Conclusion & Final Answer
The mammogram findings are most indicative of a malignant process, specifically breast cancer, due to the presence of clustered microcalcifications within an area of irregular density.

### 5. Final correct choice
B. Clustered microcalcifications within an area of irregular density

Benchmark Results

MadriMed 1.2 was rigorously evaluated across 7 medical image and text benchmarks.

🔬 Multimodal & Visual Question Answering

VQA Comparison with 4B

📝 Text only Benchmarks

SLAKE Per-Category Performance

Medmcqa (Text-Only) Per-Category Performance

By Choice Type

By Subject

Evaluation NoteBook

Technical Specifications

Benchmark Dataset Citations

1. SLAKE: BoKelvin/SLAKE
2. MedXpertQA: TsinghuaC3I/MedXpertQA
3. VQA-RAD: flaviagiammarino/vqa-rad
4. PubMedQA: qiaojin/PubMedQA
5. MedQA (USMLE): GBaker/MedQA-USMLE-4-options
6. MedMCQA: openlifescienceai/medmcqa
7. Path-VQA: flaviagiammarino/path-vqa

Limitations & Safety Guidelines

Research & Educational Use Only

MadriMed 1.2 is an experimental research model. It is not certified for autonomous clinical diagnostic decision-making.

No Treatment Prescriptions

The model should not be used as an authoritative source for:

• Pharmaceutical dosages
• Surgical interventions
• Direct treatment pathways
• Other high-stakes clinical decisions

All model-generated interpretations, medical reasoning, and visual inferences should be reviewed and independently confirmed by a qualified, licensed medical professional before being used in a clinical context.

Citation

@software{madrimed1.2-VL-2B,
  title = {MadriMed 1.2: Advanced Multimodal Medical Reasoning at the 2B Scale},
  author = {Madrisight},
  year = {2026},
  url = {https://huggingface.co/madrisight/madrimed1.2-VL-2B}
}