# Med-PaLM
Med-PaLM a generalist biomedical AI system

![Med palm](image-9.png)

## Getting Started

The following instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.

`pip install MedPalm`


### Usage


```python
import torch
from med_palm import MedPalm

# Initialize the model
model = MedPalm()

# Assume we have tokenized inputs
text_tokens = torch.tensor([[1, 2, 3, 4, 5]])  # Example tensor, replace with your actual tensor
images = torch.randn(1, 3, 224, 224)  # Example tensor, replace with your actual tensor

# Perform a forward pass on the model
output = model(text_tokens, images)

# Now `output` contains the model's predictions
print(output)

```
Remember to adjust these examples to fit the specific requirements of your project and data.

# Datasets
* [Head over to here for a dataset strategy](docs/DATASETS.md)

## System Architecture

The primary purpose of Med-PaLM is to bridge the gap between diverse data types in medicine. The architecture is designed to natively handle textual, imaging, and genomic data types.

At the core of the system is a large multimodal generative model which has a flexible structure allowing it to encode and interpret biomedical data. The model is trained on a curated dataset, the MultiMedBench, which includes 14 different tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling.

## Commercial Applications

Med-PaLM has a wide range of potential commercial applications, from improving diagnostic accuracy and speed to offering new insights into complex medical cases.

- **Clinical Diagnostics**: With its multimodal approach, Med-PaLM can provide high accuracy diagnostic solutions, integrating information from different sources such as medical imaging, patient history, and genetic data.

- **Healthcare Research**: Med-PaLM's ability to integrate and interpret diverse datasets makes it an invaluable tool in biomedical research, enabling researchers to find novel connections and insights.

- **Telemedicine**: By providing quick and reliable analysis of medical data, Med-PaLM can potentially play a crucial role in telemedicine, helping provide healthcare services remotely.


## Contributing

Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.

## License

This project is licensed under the MIT- see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

We would like to thank the numerous clinicians and data scientists who have contributed their expertise and time to help build and refine Med-PaLM.

This project represents a significant milestone in the ongoing effort to build generalist biomedical AI systems, and we look forward to seeing how it will shape the future of healthcare.
