metadata

language:
  - en
library_name: transformers
tags:
  - jiviai
  - medical ai
widget:
  - example_title: X-ray Report Generation
    messages:
      - role: user
        content:
          - type: text
            text: Generate an xray report for the above xray.
          - type: image_url
            image_url:
              url: >-
                https://jarvis-01j48hrq5383vpdk8csp3r60xa.s3.amazonaws.com/dev/MISC/2024-10-04/01J9BCTFZBH1AMD246G55DCY93.jpeg
      - role: assistant
        content: >
          Patient: [Name not provided]

          Gender: Female

          Age: [Age not provided]

          Clinical History: [Not provided]

          View: PA (Posterior-Anterior)

          Image Quality: The image is of good quality. The patient positioning
          is adequate, and the exposure is appropriate for interpretation.

          Findings:
              * The lung fields are clear bilaterally, with no evidence of consolidation, masses, or infiltrates.
              * The heart size is within normal limits, and the cardiac silhouette is unremarkable.
              * The diaphragm and costophrenic angles are clear.
              * A pacemaker and leads are present in the right upper chest, with the pacemaker generator in the left upper chest.
              * No acute bony abnormalities are identified.
              * No pneumothorax, pleural effusion, or consolidation is seen.

          Conclusion:
              * No acute cardiopulmonary abnormality is detected.
              * The presence of a pacemaker and leads suggests a history of cardiac rhythm management.
              * Clinical correlation is advised for further management.
pipeline_tag: image-text-to-text

Jivi-RadX: Large Language Vision Assistant for Xrays

Introducing Jivi-RadX-v1, an advanced visual language model specifically designed for sophisticated image reasoning in the healthcare domain. With its robust capabilities, this model excels at interpreting radiographic X-ray images, offering accurate and insightful responses to a wide range of diagnostic and analytical questions. Whether it's aiding clinicians in making informed decisions or assisting researchers in understanding complex image data, Jivi-RadX-v1 pushes the boundaries of medical imaging analysis.

Model Architecture: Jivi-RadX-v1 is built on top of Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. To support image recognition tasks, we use a separately trained vision encoder and a vision projector that integrates with our base language model.

Benchmarks

We have released our x-ray benchmark jivi_chexnet which combines rich and diverse x-ray images along with verified question and answer on them. Below is the comparision of our model with other open-source and closed-source vision LLMs.

Model	CheXpert (Stanford dataset)	NIH Chest X-Ray (NIH UK Dataset)	Overall Accuracy
Jivi AI (Jivi-RadX)	85%	64.5%	75%
Open AI (GPT-4o)	81.5%	66.3%	74%
Stanford AIMI (ChexAgent-8b)	55.6%	50.3%	53%
Microsoft (Llava-Med)	51%	46.6%	49%
*Google (Gemini 1.5 Pro)	36%	58%	47%

*Gemini 1.5 Pro occasionally declines to respond to medical questions, which affects its overall accuracy.

*Benchmark numbers were calculated using lmms-eval by LMMs-Lab.

Training process:

Training Data

Jivi-RadX-v1 was pretrained on 365k medical image and text pairs. The instruction tuning data includes over 280k synthetically generated examples.

Synthetic Data Generation

We leveraged various closed-source and open-source visual LLMs and used the metadata of the x-ray images to generate rich captions for training.

How to use

Use with transformers

Please ensure transformers>=4.45.2

import requests
import torch
from PIL import Image
from transformers import (AutoProcessor, AutoTokenizer,
                          LlavaForConditionalGeneration)

conversation = [
    {"role": "system", "content": "You a helpful AI assistant."},
    {
        "role": "user",
        "content": "<image>\n Please describe this x-ray.",
    },
]

model_id = "jiviai/Jivi-RadX-v1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(
    model_id, attn_implementation="eager", device_map="cuda", torch_dtype=torch.float16
)


prompt = tokenizer.apply_chat_template(
    conversation, tokenize=False, add_generation_prompt=True
)

url = "https://jarvis-01j48hrq5383vpdk8csp3r60xa.s3.amazonaws.com/dev/MISC/2024-10-03/01J991DRQ2G5TAB24A9QNMFAXN.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(
    model.device, dtype=model.dtype
)
# Generate
generate_ids = model.generate(**inputs, max_new_tokens=30)
output = processor.decode(
    generate_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output)

Supported Languages: Currently we only support english. We are planning to introduce multi-lingual support shortly.

Feedback: To send any feedback/questions please use the community section of the model.

Intended use

The data, code, and model checkpoints are intended to be used solely for:

Future research on visual-language processing.
Reproducibility of the experimental results reported in the reference paper.

jiviai
/

Jivi-RadX-v1