What we’re about
## ABOUT US
Welcome to Healthi AI, where our passion for health and sustainability drives our mission to create positive change in North America. Committed to a brighter and healthier future, we are a non-profit organization dedicated to addressing pressing health and sustainability issues across the continent.
## OUR MISSION
At Healthi AI, we believe that everyone deserves access to a healthy and sustainable lifestyle. Our mission is to inspire and empower individuals, communities, and organizations to make informed choices that contribute to the well-being of both people and the planet.
## WHAT DRIVES US
The urgent need for action on health and sustainability issues in North America is what fuels our commitment. From the health disparities that impact vulnerable populations to the environmental challenges threatening our ecosystems, we are driven by the understanding that collective efforts can lead to meaningful and lasting change.
## OUR APPROACH
We take a holistic approach to address the interconnected issues of health and sustainability. Through research, education, and collaborative initiatives, we aim to raise awareness, foster sustainable practices, and advocate for policies that prioritize the well-being of both individuals and the environment.
## KEY FOCUS AREAS
- Community Health: We work to improve access to healthcare, promote preventive measures, and address the social determinants of health that impact underserved communities.
- Environmental Sustainability: Our initiatives focus on promoting eco-friendly practices, conservation efforts, and advocating for policies that protect and preserve our natural resources.
- Education and Awareness: We believe in the power of knowledge. Through educational programs and awareness campaigns, we strive to empower individuals and communities to make informed choices for a healthier and more sustainable future.
- Collaborative Partnerships: We actively seek partnerships with like-minded organizations, businesses, and government entities to amplify our impact. Together, we can achieve more than any one organization or individual can accomplish alone.
## OUR IMPACT
Through the tireless efforts of our dedicated team and the support of our partners, Health Innovators has made significant strides in addressing health and sustainability challenges. Whether it's providing healthcare resources to underserved communities or leading initiatives to reduce carbon footprints, we measure our success by the positive impact we have on the lives of individuals and the health of our planet.
Upcoming events (4+)
See all- Lets build OpenHealthChatLLM together! Opensource Health Chat Language ModelNeeds location
### Overview:
OpenHealthChatLLM is an open-source large language model (LLM) specifically designed for healthcare chat applications. It aims to provide accurate, reliable, and context-aware responses to inquiries related to medical information, health advice, symptom analysis, and more. The model will be trained on a diverse dataset sourced from reputable medical literature, clinical guidelines, and anonymized patient data (in compliance with privacy regulations) to ensure its effectiveness and safety in providing healthcare-related information.
### Project Structure:
Folders within the repository for different components: data: This folder will store the training data for the LLM. Focus on collecting publicly available healthcare chat conversations, medical information resources, and relevant research papers. Ensure proper anonymization of any patient data. code: This folder will hold the scripts for training, fine-tuning, and deploying the LLM. We will consider using open-source libraries like Transformers (https://huggingface.co/docs/transformers/en/index) and libraries for medical text processing. docs: This folder will include documentation on using the LLM, including installation instructions, API details, and usage examples. evaluations: This folder will store the results of performance evaluations on the LLM, including metrics relevant to healthcare chat applications (e.g., accuracy, safety, bias detection).
### Features:
- Context-aware Responses: OpenHealthChatLLM understands the context of the conversation and provides relevant responses tailored to the user's inquiries.
- Medical Knowledge Base: The model is trained on a vast repository of medical knowledge, covering various medical specialties and topics.
- Privacy and Security: OpenHealthChatLLM prioritizes user privacy and data security, ensuring compliance with healthcare regulations such as HIPAA (Health Insurance Portability and Accountability Act).
- Customization: Users can fine-tune the model for specific healthcare domains or integrate additional datasets to enhance its capabilities.
- Scalability: OpenHealthChatLLM is designed to scale efficiently, allowing seamless integration into both small-scale applications and large-scale healthcare platforms.
### Contribution Guidelines:
We welcome contributions from developers, researchers, and healthcare professionals to improve OpenHealthChatLLM. Contributions can include but are not limited to:
- Model Enhancements: Improving the model's accuracy, performance, and efficiency.
- Data Collection and Annotation: Adding new datasets and annotating existing ones to expand the model's knowledge base.
- Privacy and Security Improvements: Implementing robust privacy measures and security protocols.
- Documentation: Writing and updating documentation to facilitate usage and development.
- Bug Fixes: Identifying and fixing bugs to ensure the reliability of the model.
### License:
OpenHealthChatLLM is licensed under the MIT License.
### Contact:
For inquiries or suggestions, please contact the project maintainers at kal@healthiai.org.
- Lets build OpenHealthChatLLM together! Opensource Health Chat Language ModelNeeds location
### Overview:
OpenHealthChatLLM is an open-source large language model (LLM) specifically designed for healthcare chat applications. It aims to provide accurate, reliable, and context-aware responses to inquiries related to medical information, health advice, symptom analysis, and more. The model will be trained on a diverse dataset sourced from reputable medical literature, clinical guidelines, and anonymized patient data (in compliance with privacy regulations) to ensure its effectiveness and safety in providing healthcare-related information.
### Project Structure:
Folders within the repository for different components: data: This folder will store the training data for the LLM. Focus on collecting publicly available healthcare chat conversations, medical information resources, and relevant research papers. Ensure proper anonymization of any patient data. code: This folder will hold the scripts for training, fine-tuning, and deploying the LLM. We will consider using open-source libraries like Transformers (https://huggingface.co/docs/transformers/en/index) and libraries for medical text processing. docs: This folder will include documentation on using the LLM, including installation instructions, API details, and usage examples. evaluations: This folder will store the results of performance evaluations on the LLM, including metrics relevant to healthcare chat applications (e.g., accuracy, safety, bias detection).
### Features:
- Context-aware Responses: OpenHealthChatLLM understands the context of the conversation and provides relevant responses tailored to the user's inquiries.
- Medical Knowledge Base: The model is trained on a vast repository of medical knowledge, covering various medical specialties and topics.
- Privacy and Security: OpenHealthChatLLM prioritizes user privacy and data security, ensuring compliance with healthcare regulations such as HIPAA (Health Insurance Portability and Accountability Act).
- Customization: Users can fine-tune the model for specific healthcare domains or integrate additional datasets to enhance its capabilities.
- Scalability: OpenHealthChatLLM is designed to scale efficiently, allowing seamless integration into both small-scale applications and large-scale healthcare platforms.
### Contribution Guidelines:
We welcome contributions from developers, researchers, and healthcare professionals to improve OpenHealthChatLLM. Contributions can include but are not limited to:
- Model Enhancements: Improving the model's accuracy, performance, and efficiency.
- Data Collection and Annotation: Adding new datasets and annotating existing ones to expand the model's knowledge base.
- Privacy and Security Improvements: Implementing robust privacy measures and security protocols.
- Documentation: Writing and updating documentation to facilitate usage and development.
- Bug Fixes: Identifying and fixing bugs to ensure the reliability of the model.
### License:
OpenHealthChatLLM is licensed under the MIT License.
### Contact:
For inquiries or suggestions, please contact the project maintainers at kal@healthiai.org.
Group links
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