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    AI/ML-Enabled Medical Device

    Medical device that uses artificial intelligence or machine learning to perform its intended use.

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

    Definition

    An AI/ML-enabled device incorporates models that learn patterns from data to provide diagnostic, predictive, monitoring, or therapeutic functions. FDA maintains a public list of AI/ML-enabled devices that have been authorized.
    What the regulation says
    AI/ML-enabled medical devices are regulated based on their intended use and risk classification, similar to other medical devices. The FDA, for example, provides guidance on "Clinical Decision Support Software" and "Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)" to clarify regulatory expectations for these technologies. Key considerations include data management, algorithm transparency, validation, and performance monitoring, as described in documents like the IMDRF "Software as a Medical Device (SaMD): Key Definitions" guidance.

    What this means in practice

    Most authorized AI devices today are 'locked' - the model weights do not change post-deployment. Adaptive systems require a Predetermined Change Control Plan (PCCP) to enable continued learning while maintaining oversight.

    Examples

    • An AI/ML-enabled diagnostic device uses a locked algorithm trained on a large dataset of medical images to identify anomalies, requiring no post-market model updates.
    • An adaptive AI/ML therapeutic device for personalized drug dosing continuously learns from a patient's physiological data to adjust treatment, operating under an FDA-approved Predetermined Change Control Plan (PCCP).
    • A predictive AI/ML monitoring device analyzes continuous patient data streams to forecast potential adverse events, necessitating regular performance monitoring and validation to ensure accuracy.
    Common pitfalls
    • •Failing to establish a robust data management plan for training and validation data can lead to biased or ineffective AI/ML models.
    • •Assuming that an AI/ML model’s performance in a controlled development environment will perfectly translate to real-world clinical settings is a common misconception.
    • •Neglecting to implement a post-market surveillance strategy for AI/ML performance can result in undetected degradation or errors over time.
    • •Underestimating the cybersecurity risks associated with AI/ML models, including data poisoning or adversarial attacks, can compromise device safety and effectiveness.
    • •Not adequately documenting the AI/ML model’s design, development, and validation processes can lead to difficulties in demonstrating regulatory compliance.

    Frequently asked questions

    The primary regulatory concern is ensuring the safety and effectiveness of the device, particularly given the dynamic and data-driven nature of AI/ML models. Regulators focus on robust validation, risk management, and post-market surveillance.

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    Learning paths (1)

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    FDA·2MDCG·1
    1. 1
      FDA AI/ML-Enabled Device List
      Verified
      FDAfda.gov
    2. 2
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu
    3. 3
      FDA - Software as a Medical Device (SaMD)
      Verified
      FDAfda.gov

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