Clinical AI Model Card Template: What Health Systems Should Require From Vendors
A clinical AI model card template is a standardized vendor disclosure document that describes what a clinical AI model is intended to do, what data it was developed and evaluated on, how it performs, where it should not be used, how it will be monitored, and how changes will be governed. Health systems should require the model card before contract execution or deployment so governance, compliance, clinical informatics, and procurement teams can evaluate patient safety risk, regulatory alignment, operational fit, and ongoing accountability.
Why model cards belong in clinical AI procurement
Clinical AI procurement requires more than a product description or a performance claim. A model card gives governance, compliance, clinical informatics, and procurement teams a consistent way to review the intended use, evidence base, operating constraints, and lifecycle responsibilities associated with a clinical AI model before deployment.
Core fields every clinical AI model card should include
The template should require vendors to separate model identity, intended clinical workflow, development data, evaluation data, performance evidence, and lifecycle controls. These fields help reviewers understand whether the AI system is appropriate for the proposed care setting and whether accountability after deployment is clearly assigned.
- Model identity and ownership: Document the model name, version, vendor owner, regulatory status if applicable, clinical owner, deployment environment, and whether the AI function is standalone or embedded in another health IT system.
- Intended use and clinical workflow: Specify the clinical task, target users, patient population, care setting, input data, output format, decision role, and known contraindications or out-of-scope uses.
- Development and training data: Describe data sources, time period, population characteristics, labeling approach, inclusion and exclusion criteria, missingness handling, and any known limitations in representativeness.
- Evaluation and validation data: Separate independent validation data from training data. Include evaluation setting, cohort characteristics, comparison method, local validation status, and whether the evidence reflects the proposed health system context.
- Performance and subgroup results: Report clinically relevant metrics, thresholds, confidence considerations where available, subgroup performance where feasible, failure modes, and conditions that may reduce reliability.
- Lifecycle, monitoring, and change control: Define model versioning, planned updates, change boundaries, monitoring metrics, drift indicators, escalation paths, audit logs, rollback expectations, and responsibility split between vendor and health system.
How governance teams should read the template
Governance teams should treat the model card as an evidence and accountability document, not as a marketing summary. Reviewers can compare what the vendor discloses against the proposed clinical workflow, patient population, implementation plan, and monitoring responsibilities. Missing or vague fields should be resolved before contract execution or deployment.
Runtime fields needed after deployment
After deployment, model card information should connect to runtime governance. Versioning, monitoring metrics, drift indicators, escalation paths, audit logs, rollback expectations, and responsibility splits should remain available to teams responsible for operating and overseeing the AI system.
Evaluation criteria for accepting or rejecting a vendor submission
A vendor submission should be accepted only when the model card is specific enough for governance, compliance, clinical informatics, and procurement teams to evaluate patient safety risk, regulatory alignment, operational fit, and ongoing accountability. If the submission does not distinguish training, testing, external validation, and local validation, or if it does not define intended use, prohibited use, clinician role, monitoring, and change control, teams should require clarification before proceeding.
Minimum vendor disclosure areas
These areas summarize the information health systems should expect vendors to provide in a clinical AI model card.
- Use Intended clinical purpose, user, workflow, and exclusion criteria.
- Data Development, training, testing, and validation data described separately.
- Performance Aggregate and subgroup evaluation results tied to model version.
- Change Versioning, update process, and any predetermined change approach.
- Monitoring Drift, degradation, escalation, and audit responsibilities after deployment.
Clinical AI procurement checklist for vendor model cards
- Require a completed model card for each model and material model version, including embedded AI functionality inside larger platforms.
- Verify that training, testing, external validation, and local validation are described as distinct evidence categories.
- Confirm that intended use, prohibited use, clinician role, and patient population are specific enough to translate into policy and workflow controls.
- Require subgroup performance or a documented explanation of why subgroup evaluation is not available or not applicable.
- Tie model updates to a documented change control process and require a revised model card when model behavior or intended use changes.
- Assign monitoring, drift response, audit logging, issue escalation, and rollback responsibilities in contract language.
Strengthen runtime governance around clinical AI
A model card defines what a vendor claims about a clinical AI model. Runtime governance helps enforce how AI systems behave after deployment, including monitoring, permissions, controls, and auditability.
Explore Runtime Governance