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CHAI Framework

Applied Model Card

A standardized transparency format for health AI solutions—covering intended use, performance, risks, and responsible AI principles.

1

Overview

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.

2

Resources

Current draft versions of the CHAI Applied Model Card.

Complete Documentation

Full instructions, references, and guidance for the model card.

Complete Documentation - Word Version

Editable Word version of the complete documentation.

Model Card Template and Schema (GitHub repository)

Template files and schema for building and validating model cards.

Completed Example

Aidoc ICH-02-RT example showing a completed Applied Model Card.

3

Example

Completed model card

Aidoc ICH-02-RT

See how a real solution presents intended use, performance metrics, risks, and governance details in the standardized Applied Model Card format.

View example PDF
4

Registry adoption

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

Centralized access to AI model cards

Greater transparency

Greater transparency in AI development, risks, and performance.

Open access

Open access for anyone—health systems, vendors, and the public—supporting more informed AI adoption.

View supporting organizations (36)
  • 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

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.