Applied Model Card
Description
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.
Output
Current draft versions of CHAI Applied Model Card are linked here:
Complete Documentation: Available Here
Model Card Template and Schema (GitHub repository): Available Here
Completed Example: Available Here
On Friday Feb 28, CHAI announced its partnership with Avanade on the first-ever public registry for Health AI governance. This initiative is backed by a number of our partners:
Aidoc
Ambience Healthcare
American Heart Association
Avanade
BeeKeeperAI
Bend Health
Better Evidence, The Global Health Delivery Project at Harvard
Biotale Solutions
Booz Allen Hamilton
Cleveland Clinic
Complear
Dandelion Health
Duke Health
Dyna AI
Ferrum Health
Gesund.ai
Healthvana
Innovaccer
Iodine Software
Kaiser Permanente
Lyric AI
Memorial Sloan Kettering
Mercy
Mount Sinai Health System
Nabla
National Health Council
OrbDoc
Penguin Ai
Providence
Rush University System for Health
Sharp HealthCare
Stanford Medicine
Surescripts
ThetaRho, Inc
UMass Memorial
University of Texas Health System
This means:
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 want to remind all of you that this is an iterative process and we are committed to continuous improvement. As we start building out the registry, we will engage all our members with focused sessions to learn about core functionalities, ways to integrate this into workflows, and any other feedback you might have to make it most useful for all of the community.
Read more online in Newsweek or Fierce Healthcare.
