What Is the FDA Regulatory Framework for AI Medical Devices?

Author: Rasit Dinc

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the healthcare landscape, with medical devices being a key area of innovation. These technologies offer the potential to derive new and important insights from the vast amount of data generated during the delivery of health care every day. As AI/ML-enabled medical devices become more prevalent, the U.S. Food and Drug Administration (FDA) has been actively developing a regulatory framework to ensure their safety and effectiveness. This article provides an overview of the FDA's regulatory approach to AI medical devices, within the broader context of global regulatory efforts [1].

The FDA regulates medical devices through a risk-based framework, and AI-enabled devices are no exception. The agency's traditional paradigm of medical device regulation, however, was not designed for the adaptive nature of many AI/ML technologies. To address this, the FDA has introduced a series of guidance documents and action plans to provide clarity and a structured approach for manufacturers.

In April 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)," which outlined a potential approach for premarket review of AI/ML-driven software modifications. This was followed by the "Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan" in January 2021 [2]. This action plan detailed a multi-pronged approach to tailored regulatory oversight of AI/ML-based SaMD.

Key principles of the FDA's framework include a total product lifecycle (TPLC) approach, which encompasses everything from initial design and development to post-market surveillance. The agency has also emphasized the importance of Good Machine Learning Practice (GMLP), which are a set of guiding principles to promote safe, effective, and high-quality medical devices that use AI/ML. These principles are intended to be harmonized with international standards and best practices.

A significant component of the FDA's framework is the concept of a Predetermined Change Control Plan (PCCP). A PCCP is a plan that a manufacturer submits to the FDA as part of a premarket submission. It describes the anticipated modifications to an AI/ML-enabled device, the methodology for implementing and validating those changes, and the assessment of the impact of those changes. This allows for a more streamlined process for implementing certain changes that were anticipated and reviewed by the FDA, without requiring a new premarket submission for each modification.

The FDA has also stressed the importance of transparency in AI/ML-enabled medical devices. The agency has issued guiding principles on transparency to help ensure that users, including healthcare professionals and patients, have a clear understanding of the device's functionality, performance, and limitations. This includes providing information about the data used to train the algorithm, the logic of the algorithm, and the device's performance characteristics.

In recent years, the FDA has continued to refine its approach. In March 2024, the agency published a paper on its coordinated approach to AI across its different centers, and in January 2025, it released a draft guidance on lifecycle management and marketing submission recommendations for AI-enabled device software functions. These documents demonstrate the FDA's ongoing commitment to fostering innovation while ensuring that AI medical devices are safe and effective for their intended use [2].

In conclusion, the FDA's regulatory framework for AI medical devices is a dynamic and evolving system that seeks to balance the need for innovation with the imperative of patient safety. By embracing a TPLC approach, promoting GMLP, and introducing concepts like PCCPs, the FDA is working to create a clear and predictable pathway for the development and deployment of these transformative technologies. As AI continues to advance, it is crucial for all stakeholders, including manufacturers, healthcare providers, and patients, to stay informed about the FDA's regulatory landscape.

References

[1] Rahi, S., Lather, V., Rana, A., & Pandit, J. (2025). Comprehensive framework for the global regulation and approval of AI-integrated medical devices. Applied Drug Research, Clinical Trials and Regulatory Affairs, 11(1). https://doi.org/10.2174/0126673371347630250418134235

[2] U.S. Food and Drug Administration. (2025, March 25). Artificial Intelligence in Software as a Medical Device. FDA. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device