Artificial Intelligence and Machine Learning in Software as a Medical Device

Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The complex and dynamic processes involved in the development, deployment, use, and maintenance of AI technologies benefit from careful management throughout the medical product life cycle.

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What Is Artificial Intelligence and Machine Learning?

Artificial Intelligence is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.

Machine Learning is a set of techniques that can be used to train AI algorithms to improve performance at a task based on data.

Some real-world examples of artificial intelligence and machine learning technologies include:

  • An imaging system that uses algorithms to give diagnostic information for skin cancer in patients.
  • A smart sensor device that estimates the probability of a heart attack.

Additional information can be found at Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.

How Are Artificial Intelligence and Machine Learning (AI/ML) Transforming Medical Devices?

AI/ML technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance.

How Is the FDA Considering Regulation of Artificial Intelligence and Machine Learning Medical Devices?

The FDA reviews medical devices through an appropriate premarket pathway, such as premarket clearance (510(k)), De Novo classification, or premarket approval. The FDA may also review and clear modifications to medical devices, including software as a medical device, depending on the significance or risk posed to patients of that modification. Learn the current FDA guidance for risk-based approach for 510(k) software modifications.

The FDA’s traditional paradigm of medical device regulation was not designed for adaptive artificial intelligence and machine learning technologies. Many changes to artificial intelligence and machine learning-driven devices may need a premarket review.

On April 2, 2019, the FDA published a discussion paper “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback” that describes a potential approach to premarket review for artificial intelligence and machine learning-driven software modifications.

In January 2021, the FDA published the “Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan” or “AI/ML SaMD Action Plan.” Consistent with the action plan, the FDA later issued the following documents:

On March 15, 2024 the FDA published the “Artificial Intelligence and Medical Products: How CBER, CDER, CDRH, and OCP are Working Together,” which represents the FDA’s coordinated approach to AI. This paper is intended to complement the “AI/ML SaMD Action Plan” and represents a commitment between the FDA’s Center for Biologics Evaluation and Research (CBER), the Center for Drug Evaluation and Research (CDER), and the Center for Devices and Radiological Health (CDRH), and the Office of Combination Products (OCP), to drive alignment and share learnings applicable to AI in medical products more broadly.

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