Artificial intelligence and machine learning 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 FDA is considering a total product lifecycle-based regulatory framework for these technologies that would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device is maintained.
On this page:
- What is Artificial Intelligence and Machine Learning?
- How are Artificial Intelligence and Machine Learning Transforming Medical Devices?
- How is the FDA Considering Regulation of Artificial Intelligence and Machine Learning Medical Devices?
- Artificial and Machine Learning News and Updates
- Contact Us
Artificial Intelligence has been broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs (McCarthy, 2007). Artificial intelligence can use different techniques, including models based on statistical analysis of data, expert systems that primarily rely on if-then statements, and machine learning.
Machine Learning is an artificial intelligence technique that can be used to design and train software algorithms to learn from and act on data. Software developers can use machine learning to create an algorithm that is ‘locked’ so that its function does not change, or ‘adaptive’ so its behavior can change over time based on new 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 electrocardiogram (ECG) device that estimates the probability of a heart attack.
Adaptive artificial intelligence and machine learning technologies differ from other software as a medical device (SaMD) in that they have the potential to adapt and optimize device performance in real-time to continuously improve health care for patients. The International Medical Device Regulators Forum (IMDRF) defines software as a medical device as software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device. The FDA under the Federal Food, Drug, and Cosmetic Act (FD&C Act) considers medical purpose as those purposes that are intended to treat, diagnose, cure, mitigate, or prevent disease or other conditions.
How is the FDA Considering Regulation of Artificial Intelligence and Machine Learning Medical Devices?
Traditionally, 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. Under the FDA’s current approach to software modifications, the FDA anticipates that many of these artificial intelligence and machine learning-driven software changes to a device 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 the FDA’s foundation for a potential approach to premarket review for artificial intelligence and machine learning-driven software modifications.
The ideas described in the discussion paper leverage practices from our current premarket programs and rely on IMDRF’s risk categorization principles, the FDA’s benefit-risk framework, risk management principles described in the software modifications guidance, and the organization-based total product lifecycle approach (also envisioned in the Digital Health Software Precertification (Pre-Cert) Program).
In this framework, the FDA introduces a “predetermined change control plan” in premarket submissions. This plan would include the types of anticipated modifications—referred to as the “Software as a Medical Device Pre-Specifications”—and the associated methodology being used to implement those changes in a controlled manner that manages risks to patients —referred to as the “Algorithm Change Protocol.”
In this approach, the FDA would expect a commitment from manufacturers on transparency and real-world performance monitoring for artificial intelligence and machine learning-based software as a medical device, as well as periodic updates to the FDA on what changes were implemented as part of the approved pre-specifications and the algorithm change protocol.
The proposed regulatory framework could enable the FDA and manufacturers to evaluate and monitor a software product from its premarket development to postmarket performance. This potential framework allows for the FDA’s regulatory oversight to embrace the iterative improvement power of artificial intelligence and machine learning-based software as a medical device, while assuring patient safety.
- Public Workshop - Evolving Role of Artificial Intelligence in Radiological Imaging: February 25 - 26, 2020
- Commissioner's Statement: Steps Toward a New, Tailored Review Framework for Artificial Intelligence-Based Medical Devices (April 2019)
- FDA Voices: New Steps to Empower Consumers and Advance Digital Healthcare (July 2017)
If you have questions about artificial intelligence, machine learning, or other digital health topics, ask a question about digital health regulatory policies.
McCarthy, J. (2007). What Is Artificial Intelligence? Stanford University, Stanford, CA. Retrieved from http://jmc.stanford.edu/articles/whatisai/whatisai.pdf