How Does AI Fit into the FDA's Digital Health Framework?

Author: Rasit Dinc

Date: December 20, 2025

Introduction

Artificial intelligence (AI) is rapidly transforming the healthcare landscape, offering unprecedented opportunities to improve patient care, diagnostics, and treatment. As AI-powered medical devices and software become increasingly prevalent, regulatory bodies like the U.S. Food and Drug Administration (FDA) are tasked with ensuring their safety and effectiveness. The FDA's Digital Health Framework provides a comprehensive approach to regulating these innovative technologies, balancing the need for patient safety with the drive for technological advancement. This article explores how AI fits into the FDA's Digital Health Framework, examining the key regulatory considerations and the future of AI in healthcare.

The FDA's Risk-Based Approach

The FDA's approach to regulating digital health technologies, including those powered by AI, is based on a risk-based framework. This means that the level of regulatory scrutiny is proportional to the risk the technology poses to patients. For example, a simple wellness app that tracks exercise would be subject to less stringent regulation than a sophisticated AI algorithm that provides diagnostic recommendations to clinicians. This risk-based approach allows the FDA to focus its resources on the highest-risk technologies while fostering innovation in lower-risk areas [1].

The FDA's traditional paradigm of medical device regulation was not designed for adaptive AI and machine learning (ML) technologies. Many changes to AI/ML-driven devices may need a premarket review. To address this, the FDA has proposed a regulatory framework for modifications to AI/ML-based Software as a Medical Device (SaMD). This framework aims to provide a clear pathway for manufacturers to update their AI algorithms without having to go through the full premarket review process each time [2].

Predetermined Change Control Plans

A key component of the FDA's proposed framework is the use of Predetermined Change Control Plans (PCCPs). A PCCP is a plan that a manufacturer submits to the FDA as part of the initial premarket review for an AI/ML-based SaMD. The PCCP specifies the types of modifications the manufacturer intends to make to the algorithm, the methodology for implementing and validating those changes, and the procedures for documenting the changes. If the FDA agrees to the PCCP, the manufacturer can then make the specified changes without having to submit a new premarket application for each modification [3].

This approach allows for a more streamlined and efficient regulatory process, enabling manufacturers to continuously improve their AI algorithms based on real-world data while still ensuring patient safety. The FDA has issued final guidance on Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions, which provides recommendations for the content of a PCCP [4].

Transparency and Real-World Performance Monitoring

Transparency is another critical aspect of the FDA's approach to regulating AI in digital health. The FDA believes that it is essential for users, including both patients and healthcare providers, to have a clear understanding of how an AI-powered device works and what its limitations are. This includes providing information about the data that was used to train the algorithm, the performance of the algorithm, and the potential for bias [5].

In addition to premarket review, the FDA also emphasizes the importance of real-world performance monitoring for AI-powered devices. This involves collecting and analyzing data on the performance of the device after it has been deployed in a clinical setting. This data can be used to identify any potential safety or performance issues and to inform future updates to the algorithm. The FDA's focus on real-world performance monitoring reflects the understanding that AI algorithms can evolve and change over time, and that it is essential to have a system in place to monitor their performance in the real world [6].

Conclusion

The integration of AI into the FDA's Digital Health Framework represents a significant step forward in the regulation of these innovative technologies. The FDA's risk-based approach, use of PCCPs, and emphasis on transparency and real-world performance monitoring provide a clear and flexible framework for ensuring the safety and effectiveness of AI-powered medical devices. As AI continues to evolve, it is likely that the FDA's regulatory framework will also continue to adapt and evolve to meet the challenges and opportunities of this rapidly changing field.

References

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

[2] U.S. Food and Drug Administration. (2019, April 2). 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. FDA. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device

[3] U.S. Food and Drug Administration. (2023, October). Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles. FDA. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device

[4] U.S. Food and Drug Administration. (2024, December). Final Guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions. FDA. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device

[5] U.S. Food and Drug Administration. (2024, June). Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles. FDA. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device

[6] U.S. Food and Drug Administration. (2021, January). Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan. FDA. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device