As healthcare organizations accelerate AI adoption, they're being asked to evaluate more AI solutions than ever before. Whether the use case is ambient documentation, clinical decision support, or administrative automation, one question remains consistent:
How do you know if an AI model is appropriate for your organization?
One tool that's becoming increasingly important in answering that question is the model card.
While model cards won't tell you everything you need to know about an AI solution, they provide a standardized way for vendors to communicate how a model was developed, evaluated, and intended to be used. For healthcare organizations navigating AI procurement and governance, they're quickly becoming an essential part of the conversation.
What is a model card?
A model card is a document that describes an AI model's purpose, performance, evaluation, and limitations. Think of it as technical documentation written to help customers, evaluators, and governance teams understand how a model works and where it should—and shouldn't—be used.
Rather than relying on high-level marketing claims, model cards provide objective information that helps healthcare organizations ask more informed questions during the evaluation process.
What information does a model card include?
While model cards can vary by organization, they typically cover several key areas:
Intended use
What is the model designed to do? Who are the intended users? What clinical or operational workflows is it meant to support?
Validation and evaluation
How was the model tested? What datasets or benchmarks were used to evaluate performance?
Performance
How well does the model perform? What metrics were measured, and under what conditions?
Limitations
What are the known constraints or situations where the model may perform differently? Understanding limitations is just as important as understanding strengths.
Safety and risk considerations
How are potential risks identified and mitigated? What safeguards are in place to support responsible use?
Monitoring and updates
How will the model be monitored after deployment? What happens when models are updated or retrained?
Together, these elements help create a more transparent picture of how an AI solution was developed and what organizations should consider before implementing it.
Why are model cards important?
As AI becomes embedded in clinical workflows, healthcare organizations need consistent ways to evaluate new technologies.
Model cards help establish a common foundation for conversations between vendors and health systems. They allow procurement teams, clinical leaders, IT, compliance, and governance committees to review the same information using a shared framework.
They also encourage vendors to communicate more clearly about both the strengths and limitations of their models, making evaluations more efficient and informed.
What model cards don't do
While model cards are an important step toward greater transparency, they aren't a complete AI governance strategy.
A model card doesn't replace organizational due diligence, implementation planning, ongoing monitoring, or vendor partnership. It provides information, but healthcare organizations still need to determine how an AI solution aligns with their own clinical workflows, governance processes, and risk tolerance.
In other words, a model card helps organizations ask better questions. It doesn't answer every one of them.
Looking ahead
As healthcare AI continues to mature, transparency will become an increasingly important part of evaluating and deploying AI responsibly.
Model cards are just one piece of building trustworthy clinical AI. From governance and evaluation to implementation and adoption, healthcare organizations need partners who understand the broader challenges of scaling AI responsibly.
If you're exploring how to evaluate or implement clinical AI at your organization, we'd love to connect. Reach out to the Nabla team at contact@nabla.com to continue the conversation or learn more here.
