Downloads · 30 days
0
synthetic-patients/base
base is a machine learning model from synthetic-patients. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-sa-4.0.
Downloads · 30 days
0
Access
Public
Updated Jan 21, 2025
Repo size
6.6 MB
Likes
3
Public
Click a slice to open those files.
.png6.5 MB · 90%
From the Hugging Face model README

Welcome to our repository. Here, we present the code and data used to create a novel approach to simulating difficult conversations using AI-generated avatars. Unlike prior generations of virtual patients, these avatars offer an unprecedented realism and richness of conversation. Our repository contains a collection of files and links related to our work.
patient_profiles folder in this repository.code folder of this repository.To experiment with the realtime video chat application, you will need to run it locally. We have provided a docker container with the requirements. You will need API keys for both OpenAI and ElevenLabs to run this program. The program will prompt you to provide them at runtime. You will need an account to both of these services to get the keys, and you will be charged for usage. These keys will only be stored within your instance of docker and will not be shared.
To begin, make sure that you have Docker installed. For MacOS and Windows computers, we suggest Docker Desktop.
Then, from your command-line (terminal), run:
docker pull syntheticpatients/base
This will take a significant amount of time to download, as it currently is around 5GB. Once this has been completed, you can run the script by executing the following in your terminal:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/synthetic-patients/install/main/run.sh)"
This will launch the synthetic patient server using your OpenAI and ElevenLabs API. Once the server has completed launching, direct your browser to http://localhost:5000/client to begin interacting.
@misc{chu2024syntheticpatientssimulatingdifficult,
title={Synthetic Patients: Simulating Difficult Conversations with Multimodal Generative AI for Medical Education},
author={Simon N. Chu and Alex J. Goodell},
year={2024},
eprint={2405.19941},
archivePrefix={arXiv},
primaryClass={cs.HC},
url={https://arxiv.org/abs/2405.19941},
}