Med-PaLM

A responsible path to generative AI in healthcare: Unleash the power of Med-PaLM 2 to revolutionize medical knowledge, answer complex questions, and enhance healthcare experiences with accuracy, safety, and equitable practices.

Getting Started

pip install MedPalm

Usage

import torch
from medpalm.model import MedPalm

#usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 4096))

model = MedPalm()
output = model(img, text)
print(output.shape)

📝 Note: Modify the examples to suit your data and project needs.

📚 Datasets

Here is a comprehensive markdown table containing metadata and details for all the datasets mentioned in the MED PALM paper

💼 Commercial Use-Cases

Med Palm has thousands of potential use cases the 3 below are simple, for more detailed applications check out my new blog article on MedPalm's use in the real world. Click here to learn more

• Clinical Diagnostics: Combining medical imaging, patient tales 📖, and genes, we're aiming for top-notch diagnostic solutions.

• Healthcare Research: Dive deep into diverse datasets and discover something new with Med-PaLM by your side! 🤿

• Telemedicine: Quick, reliable, and remote! 🌍 Med-PaLM's here to revolutionize telehealth.

Contributing to Med Palm 🤖🌟

Help with the todo list!

License

Med-PaLM's is under the MIT license. Check out the details here.

Citation

@misc{2307.14334,
Author = {Tao Tu and Shekoofeh Azizi and Danny Driess and Mike Schaekermann and Mohamed Amin and Pi-Chuan Chang and Andrew Carroll and Chuck Lau and Ryutaro Tanno and Ira Ktena and Basil Mustafa and Aakanksha Chowdhery and Yun Liu and Simon Kornblith and David Fleet and Philip Mansfield and Sushant Prakash and Renee Wong and Sunny Virmani and Christopher Semturs and S Sara Mahdavi and Bradley Green and Ewa Dominowska and Blaise Aguera y Arcas and Joelle Barral and Dale Webster and Greg S. Corrado and Yossi Matias and Karan Singhal and Pete Florence and Alan Karthikesalingam and Vivek Natarajan},
Title = {Towards Generalist Biomedical AI},
Year = {2023},
Eprint = {arXiv:2307.14334},
}

Todo

• Implement a training script using Huggingface's accelerate and deepspeed