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The FDA’s new guidelines for machine learning-enabled medical devices, or MLMDs, build upon existing principles for good machine learning practice (GLMP).

FDA Releases New Principles for Machine Learning-Enabled Medical Devices

The new guiding principles promote transparency to build trust and improve the user experience of machine learning-enabled medical devices

U.S. Food and Drug Administration
Published:Jun 17, 2024
|3 min read
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In 2021, in collaboration with Health Canada and the UK's Medicines and Healthcare Products Regulatory Agency (MHRA), the U.S. FDA released 10 guiding principles for good machine learning practice (GMLP). The principles aim to ensure the development of safe, effective, and high-quality artificial intelligence (AI) technologies in health care.

Now, building upon that framework, the FDA, Health Canada, and MHRA have also outlined guiding principles to enhance transparency in machine learning-enabled medical devices (MLMDs). The MLMD principles specifically add onto principle 7 and 9 of the GMLP principles:

  • Principle 7: Focus is placed on the performance of the human-AI team.
  • Principle 9: Users are provided clear, essential information.

“While the guiding principles presented here promote transparency for MLMDs, transparency is a good practice to consider for all medical devices,” wrote the FDA in a June 13 statement.

Key definitions

While “transparency” refers to the degree to which appropriate information about a MLMD is communicated to audiences, “logic” pertains to information about how an output or result was reached and/or how a decision or action was made. In turn, “explainability” refers to the degree to which logic is clearly explained, whether in plain language that any person can understand, or when relevant, in more technical language for specialized clinical users.

Effective transparency:

  • ensures that information that could impact risks and patient outcomes is communicated,
  • considers the information that the intended user or audience needs and the context in which it's used,
  • uses the best media, timing and strategies for successful communication, and
  • relies on a holistic understanding of users, environments, and workflows.

The document also highlights “human-centered design” as an important concept that includes the whole user experience in the design and development process.

Human-centered design can be used to help:

  • develop MLMDs with a high degree of transparency,
  • help validate transparency, and
  • ensure that users have all of the device-related information they need.

The guiding principles for transparency of MLMDs consider the following:

  • who (relevant audiences)
  • why (motivation)
  • what (relevant information)
  • where (placement of information)
  • when (timing)
  • how (methods used to support transparency)
Summary of transparency guiding principles
Who: Relevant audiences for transparencyTransparency is relevant to all parties involved in a patient's health care, including those intended to:
  • use or receive health care with the device.
  • make decisions about the device to support patient outcomes.
Why: Motivation for transparencyTransparency supports:
  • safe and effective use.
  • patient-centered care.
  • identification and evaluation of risks and benefits of a device.
  • informed decision-making and risk management.
  • device maintenance and detection of errors or performance degradation.
  • health equity through identification of bias
  • increased fluency and confidence in MLMD use, increased adoption of the technology.
What: Relevant informationEnabling an understanding of the MLMD includes sharing relevant information on:
  • device characterization and intended use.
  • how the device fits into health care workflow, including the intended impact on the judgment of a health care professional.
  • device performance.
  • device benefits and risks.
  • product development and risk management activities across the lifecycle.
  • logic of the model, when available.
  • device limitations, including biases, confidence intervals and data characterization gaps.
  • how safety and effectiveness are maintained across the lifecycle.
Where: Placement of informationMaximizing the utility of the software user interface can:
  • make information more responsive.
  • allow information to be personalized, adaptive and reciprocal.
  • address user needs through a variety of modalities.
When: Timing of communicationTimely communication can support successful transparency, such as:
  • considering information needs at different stages of the total product lifecycle.
  • providing notifications of device updates.
  • providing targeted information when it's needed in the workflow.
How: Methods to support transparencyHuman-centered design principles can support transparency.

These updated principles aim to build trust and transparency, improve the user experience of MLMDs, and ensure that healthcare providers and patients can rely on these technologies to enhance medical decision-making and patient outcomes.

- This information was summarized for length, clarity, and style from the original, which was published on the U.S. FDA website.