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    Synthetic Data

    Artificially generated data used to augment training, validation, or stress-testing of medical AI.

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

    Definition

    Synthetic data is generated by simulators, generative models, or rule-based systems. Used to balance underrepresented subgroups, model rare events, or substitute for sensitive data - but introduces realism, leakage, and bias-amplification risks.
    What the regulation says
    Regulators recognize the utility of synthetic data for MedTech, particularly in AI/ML model development and validation as outlined in FDA guidance documents like "Content of Premarket Submissions for Device Software Functions" and "Marketing Submission Recommendations for Predicate Devices Used in Artificial Intelligence and Machine Learning-Enabled Medical Devices." However, they emphasize the need for rigorous validation to ensure the synthetic data accurately reflects real-world clinical data distributions and characteristics, without introducing new risks such as bias or reduced generalizability. The IMDRF also addresses data quality considerations, which would extend to synthetic data, underscoring the importance of its suitability for regulatory decision-making.

    What this means in practice

    FDA has explicitly noted both the promise and the limits of synthetic data for training and validation; transparent reporting is expected.

    Examples

    • Synthetic data is used to train an AI algorithm for detecting rare disease anomalies in medical images, where real patient data is scarce.
    • A manufacturer employs synthetic patient records to test the cybersecurity resilience of a new medical device software without compromising actual patient privacy.
    • Synthetic control arms are sometimes explored in clinical studies to reduce the number of patients exposed to a placebo, though this requires strong statistical justification and regulatory acceptance.
    Common pitfalls
    • •A common pitfall is generating synthetic data that fails to capture the true underlying statistical properties or rare events accurately, leading to models that perform poorly in real-world scenarios.
    • •Another mistake is using synthetic data without sufficient transparency regarding its generation methods, assumptions, and validation, which can hinder regulatory review.
    • •Failing to address potential biases amplified during synthetic data generation is a significant pitfall, as it can perpetuate or exacerbate inequities in AI/ML-enabled medical devices.
    • •Over-reliance on synthetic data without subsequent validation against real-world data can lead to a false sense of security regarding model performance and safety.
    • •A pitfall is not adequately assessing the impact of synthetic data on the overall risk management strategy of a medical device, especially regarding safety and effectiveness.

    Frequently asked questions

    The primary concern is ensuring that synthetic data maintains the validity, representativeness, and integrity of real-world data without introducing new biases, vulnerabilities, or misrepresentations that could affect the safety and effectiveness of a medical device.
    Grouped by theme

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    FDA·1IMDRF·1MDCG·1
    1. 1
      FDA Synthetic Data Discussion
      Verified
      FDAfda.gov
    2. 2
      IMDRF - Software as a Medical Device
      Verified
      IMDRFimdrf.org
    3. 3
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu

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