The Critical Role of Model Cards When Selecting an AI Vendor

As AI becomes ubiquitous in healthcare, a critical question emerges: How do you know what you’re actually getting when you implement an AI system?

The answer lies in a concept that’s become essential for responsible AI deployment: model cards.

What Is a Model Card?

A model card is a standardized document that describes an AI model’s key characteristics, including:

Intended use - What the model was designed to do
Training data - What data was used to train the model
Performance metrics - How the model performs across different conditions
Limitations - Where the model may fall short
Ethical considerations - Potential risks and mitigation strategies

Think of it as a nutrition label for AI -essential information that helps you make informed decisions.

Why Model Cards Matter in Healthcare

In healthcare, the stakes for AI implementation are uniquely high. Model cards matter because:

Patient Safety

Understanding how an AI system performs -and where it might fail -is essential for patient safety. A model that works well in one population might perform poorly in another. Without transparency about training data and validation, you’re flying blind.

Regulatory Compliance

The FDA and other regulators are increasingly focused on AI transparency. Model cards demonstrate due diligence and support compliance efforts.

Informed Decision-Making

You can’t evaluate what you can’t understand. Model cards enable meaningful comparison between AI systems and informed implementation decisions.

Accountability

When AI systems influence clinical decisions, accountability requires transparency about how those systems work.

What to Look for in a Model Card

When evaluating AI vendors for behavioral health, ask for model cards that include:

Training Data Description

What populations were included in training data?
How was data collected and labeled?
What time periods are represented?
Are there known gaps or limitations?

Performance Metrics

What validation studies have been conducted?
How does the model perform across different demographic groups?
What are the sensitivity and specificity metrics?
How does performance compare to human raters?

Intended Use

What clinical decisions is the model designed to support?
What are the appropriate use contexts?
What decisions should NOT be made based on model output?

Limitations

Where does the model perform less well?
What populations or contexts haven’t been validated?
What are known failure modes?

Ongoing Monitoring

How is performance monitored post-deployment?
What triggers model updates or retraining?
How are issues identified and addressed?

Red Flags

Be cautious of vendors who:

Refuse to provide model documentation - Transparency should be standard
Claim perfect performance - No AI system is perfect
Can’t describe training data - This suggests inadequate governance
Dismiss bias concerns - Bias is real and must be addressed
Lack validation studies - Claims require evidence

Our Approach

At Videra Health, we believe transparency builds trust. We provide detailed documentation of our AI systems, including:

Training data characteristics and sources
Validation study results with peer-reviewed publications
Performance metrics across populations
Known limitations and appropriate use guidelines
Ongoing monitoring and improvement processes

We’ve validated our systems through rigorous clinical research, publishing results in peer-reviewed journals. Our TDScreen tool, for example, demonstrated a Cohen’s Kappa of 0.61 in validation studies -actually exceeding agreement between human raters.

Questions to Ask Vendors

When evaluating AI solutions for your organization, ask:

Can you provide a model card or equivalent documentation?
What validation studies have you conducted?
How does your system perform across different patient populations?
What are the known limitations of your AI?
How do you monitor performance post-deployment?

The answers -or lack thereof -tell you a lot about whether a vendor takes AI governance seriously.

The Bottom Line

AI has genuine potential to improve behavioral health care. But realizing that potential requires informed implementation based on transparent information about how AI systems work.

Model cards make that transparency possible. Before implementing any AI system, ask for the documentation that lets you make informed decisions.