Complete Documentation
Full instructions, references, and guidance for the model card.
CHAI Framework
A standardized transparency format for health AI solutions—covering intended use, performance, risks, and responsible AI principles.
The applied model card describes an AI solution focused on the application in a health use case. This AI solution will be embedded within an AI system, which includes the fully operational AI use case, including the model(s), technical infrastructure, and personnel in the workflow.
This model card supports meeting the criteria for HTI-1 for predictive DSIs defined as “…technology that supports decision-making based on algorithms or models that derive relationships from training data and then produce an output that results in prediction, classification, recommendation, evaluation, or analysis.” In addition, this model card provides transparency for all Five of CHAI’s Principles of Responsible AI (Usefulness, Fairness, Safety, Transparency, Security & Privacy).
In this current draft release we have included: the complete documentation for the model card (includes: instructions, example, resources, references), a fillable template of the model card for stress-testing, and a separate copy of the example provided in the full documentation for reference.
Current draft versions of the CHAI Applied Model Card.
Full instructions, references, and guidance for the model card.
Editable Word version of the complete documentation.
Template files and schema for building and validating model cards.
Aidoc ICH-02-RT example showing a completed Applied Model Card.
Completed model card
See how a real solution presents intended use, performance metrics, risks, and governance details in the standardized Applied Model Card format.
On Friday, February 28, CHAI announced its partnership with Avanade on the first-ever public registry for Health AI governance. This initiative is backed by 36 partners across health systems, vendors, and collaborators.
Centralized access to AI model cards
Greater transparency in AI development, risks, and performance.
Open access for anyone—health systems, vendors, and the public—supporting more informed AI adoption.
Announced at HIMSS, this registry is set to streamline AI adoption, improve accountability, and drive meaningful partnerships across healthcare. We are committed to continuous improvement and will engage members with focused sessions on integration, workflows, and feedback.
Read more in Newsweek or Fierce Healthcare.