CHAI makes AI Model Card to simplify AI adoption

 The Coalition for Health AI (CHAI) is aiming to increase transparency and simplify the process of adopting new artificial intelligence models with a “nutrition label” that offers key information at a glance for potential users.   

The CHAI Model Card hopes to standardize the type of data that should be available to users when evaluating the origin, purpose, and strengths of AI models. It intended to function as a jumping off point during the procurement process, particularly for electronic health record (EHR) vendors that need to comply with the ONC Health IT Certification Program (HTI-1). 

The label is now available as an open source resource on GitHub, making it accessible to all developers who may wish to use it for their products. 

CHAI developed the card alongside input from clinicians, data custodians within health systems, and technical developers about what additional information beyond existing regulatory requirements would provide value when assessing an AI tool. 

“The rapid evolution of AI in healthcare has created a landscape that can feel unregulated and fragmented. CHAI’s efforts to introduce a standardized model card represent a crucial step toward ensuring transparency, safety, and trust in AI-driven clinical applications,” said Demetri Giannikopoulous, Chief Transformation Officer, Aidoc and CHAI applied model card workgroup member.  

“By establishing a common framework that aligns with federal regulations, we are moving beyond theoretical discussions and building the foundation for scalable, reliable, and ethical AI solutions that can be adopted across the healthcare ecosystem. This initiative ensures that every AI solution can be rigorously evaluated, delivering real value to clinicians, patients, and healthcare organizations alike.” 

According to CHAI, the model card includes a variety of important information, including the identity of the developer, intended uses and targeted patient populations, key performance metrics, security and compliance accreditations, maintenance requirements, known risks and out-of-scope uses, known biases, and ethical considerations, and third party information, such as relevant clinical studies, to further inform the user.  

The GitHub release of the card follows a pivotal year for artificial intelligence in healthcare, wherein a variety of leading organizations, including CHAI, released frameworks and guidance for how ethical AI should unfold across the industry. 

Ideally, the card will provide crucial transparency and information to users while supporting the development of future standards for an industry with a strong hunger for trustworthy AI tools that can reduce the complexity of clinical and administrative processes. 

“AI’s rapid advancement in healthcare is not just an opportunity — it’s a call to action,” said Christine Swisher PhD, VP Health Data Intelligence at Oracle Health and leader of the CHAI Model Card Workgroup. “We must transform principles into tangible steps that can foster trust and accountability. A common, user-friendly applied model card can help serve as a powerful tool for clinicians, health systems, and patients alike.” 

Jennifer Bresnick is a journalist and freelance content creator with a decade of experience in the health IT industry.  Her work has focused on leveraging innovative technology tools to create value, improve health equity, and achieve the promises of the learning health system.  She can be reached at jennifer@inklesscreative.com.