# Med-PaLM

In a world teeming with diverse and critical biomedical data, Med-PaLM offers a groundbreaking solution to the challenge of integrating and interpreting this data. This model is cutting-edge AI technology that creates an innovative approach for synthesizing patient data into a singular, understandable narrative, revolutionizing the way we diagnose, treat, and understand diseases. This is the promise of Med-PaLM a generalist biomedical AI system, and we are uniquely positioned to make this vision a reality

## Table of Contents
- [System Architecture](#system-architecture)
- [Commercial Applications](#commercial-applications)
- [Getting Started](#getting-started)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)

## 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.

## 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.

### Usage

Sure, let's generate a few examples on how to use the `MedPalm` model and the `MedPalmTokenizer`. Note that these examples require that you have the necessary images and texts to be processed. You may also need to adjust the dimensions of your tensors depending on the specific needs of your model. Please install necessary packages for this.

First, let's initialize the tokenizer and the model:

```python
tokenizer = MedPalmTokenizer()
model = MedPalm()
```

Next, let's tokenize a sample of text and an image. For the sake of this example, let's assume we have a dictionary `sample` that contains an image (as a PIL Image or a NumPy array) and the associated text:

```python
sample = {"image": image, "target_text": "This is a sample text."}
tokenized_sample = tokenizer.tokenize(sample)
```

Next, we can feed these tokenized samples into our model:

```python
text_tokens = tokenized_sample["text_tokens"]
images = tokenized_sample["images"]

output = model(text_tokens, images)
```

With `output`, you now have the model's predictions that you can use for further processing or evaluation.

To evaluate various metrics like sequence and scale, you may use the following hypothetical code. Note that actual implementation will depend on your specific metric calculation requirements:

```python
# Define a function for your metric calculation, e.g., sequence length
def sequence_length_metric(text_tokens):
    return len(text_tokens)

# Apply the metric to your data
sequence_length = sequence_length_metric(text_tokens)
print(f"Sequence length: {sequence_length}")
```

```python
# Define a function for your scale metric, e.g., tensor size
def tensor_scale_metric(tensor):
    return tensor.size()

# Apply the metric to your data
tensor_scale = tensor_scale_metric(images)
print(f"Tensor scale: {tensor_scale}")
```

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)
## 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.