A comprehensive, vendor-neutral AI implementation framework for the Healthcare and Life Sciences industry, enabling secure, compliant, and scalable adoption of advanced AI technologies across major cloud platforms such as Azure and AWS.
The Healthcare AI Implementation Standards (HAIIS) enables healthcare organizations to securely adopt and scale advanced AI technologies—including generative AI and machine learning—regardless of their underlying cloud infrastructure choices. This framework is informed by real-world experience with healthcare clients and is publicly available online at no cost.
Reusable technical blueprints that embed regulatory requirements (HIPAA, GxP) directly into AI implementation patterns.
Comprehensive mapping between AI components and security requirements across different cloud providers, enabling organizations to implement consistent security controls regardless of cloud platform.
Standardized approaches for managing sensitive data across AI training, inference, and monitoring processes. These protocols maintain compliance while enabling innovation in healthcare AI systems.
Structured approach to evaluate and mitigate risks specific to AI deployments in regulated environments, incorporating both technical and governance considerations.
Step-by-step guides tailored to specific industry use cases that provide concrete implementation paths for healthcare organizations.
This repository contains:
site/- Web application showcasing the frameworkLICENSE- Project license information
The framework is presented through a Next.js web application located in the site/haiis/ directory. The website includes:
- Framework overview and components
- Documentation
- Collaboration information
- Privacy policy
This framework is made publicly available at no cost to support healthcare AI adoption. See the LICENSE file for details.
This framework is based on real-world healthcare AI implementation experience. For collaboration opportunities, please refer to the collaboration section on the website.
For more information about the framework or implementation support, please visit the framework website or check the documentation section.