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    Model Card

    Structured documentation of a machine learning model's intended use, performance, and limitations.

    Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 5, 2026

    Definition

    A model card summarizes a model's purpose, training and evaluation data, performance metrics across relevant subgroups, ethical considerations, and known limitations to support transparent, responsible use.
    What the regulation says
    Regulators increasingly expect transparency in AI/ML-enabled MedTech. The FDA

    What this means in practice

    Increasingly expected by regulators (e.g., FDA's transparency principles for ML-enabled devices) and procurement teams. Often combined with 'Indications-for-Use'-style cards for clinical AI.

    Examples

    • A MedTech company developing an AI diagnostic tool for radiology creates a model card detailing the training data's demographic representation and the model's sensitivity and specificity across different patient populations.
    • Before deploying an AI-powered insulin pump, the manufacturer publishes a model card outlining the algorithm's performance under various physiological conditions and known limitations, such as interaction with specific medications.
    • A hospital procurement team reviews the model card for an AI-driven surgical planning system to assess its suitability for their patient demographics and to understand its validation against relevant clinical endpoints.
    Common pitfalls
    • •Failing to update model cards when models are retrained or modified, leading to outdated or inaccurate information, is a common pitfall.
    • •Overlooking the inclusion of performance metrics for all relevant subgroups, which can mask biases or disparities, is a mistake to avoid.
    • •Presenting overly technical information without clear, concise summaries for diverse audiences can hinder effective communication.
    • •Ignoring the iterative nature of AI development and not treating the model card as a living document can lead to compliance issues.

    Frequently asked questions

    The primary purpose is to provide a structured, transparent summary of an AI/ML model's characteristics, performance, and limitations, facilitating responsible development and deployment in MedTech.

    Cross-references

    Used by

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    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    1. 1
      FDA Transparency Principles for ML Devices
      Verified
      FDAfda.gov
    2. 2
      FDA - Software as a Medical Device (SaMD)
      Verified
      FDAfda.gov
    3. 3
      FDA - AI/ML-Enabled Medical Devices
      Verified
      FDAfda.gov

    Inline markers like [1] jump to the matching reference above.