How Does the FDA Regulate AI Clinical Decision Support? Navigating the Evolving Digital Health Landscape
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into healthcare is rapidly transforming clinical practice, offering unprecedented capabilities for diagnosis, treatment planning, and patient monitoring. However, this revolution introduces a complex regulatory challenge: how does the U.S. Food and Drug Administration (FDA), with its traditional framework for medical devices, govern software that can learn and adapt? The answer lies in a nuanced, evolving approach centered on the concept of Software as a Medical Device (SaMD) and a commitment to continuous oversight [1].
Distinguishing AI CDS: SaMD vs. Non-Device Software
The FDA's regulatory authority over AI-powered clinical decision support (CDS) hinges on a critical distinction established by the 21st Century Cures Act of 2016. Not all software used in a clinical setting is regulated as a medical device.
The FDA focuses primarily on software that meets the definition of SaMD—software intended to be used for one or more medical purposes without being part of a hardware medical device [2]. Crucially, the Cures Act carved out specific exclusions for certain types of CDS software, particularly those intended to:
- Display, analyze, or print medical information, but not for the purpose of diagnosis or treatment.
- Support or provide recommendations to a healthcare professional about prevention, diagnosis, or treatment, where the professional can independently review the basis of the recommendation.
If the AI-powered CDS is intended to replace the judgment of a healthcare professional or is used for automated diagnosis without human oversight, it is likely classified as SaMD and subject to FDA regulation. This distinction is vital for developers and clinicians alike, as it determines the regulatory burden [3].
Navigating the FDA's Approval Process for AI/ML Devices
For AI CDS classified as SaMD, the FDA utilizes its established risk-based classification system, which dictates the appropriate premarket pathway. Devices are categorized into Class I, II, or III, with Class III devices posing the highest risk and requiring the most stringent review.
| FDA Device Class | Risk Level | Premarket Pathway | Typical AI CDS Examples |
|---|---|---|---|
| Class I | Low Risk | Exempt from premarket review | Simple image processing for non-diagnostic purposes. |
| Class II | Moderate Risk | 510(k) Clearance | AI algorithms that aid in the detection of a disease (e.g., stroke, diabetic retinopathy) where a predicate device exists. |
| Class III | High Risk | Premarket Approval (PMA) | AI systems that support or sustain human life, or are of substantial importance in preventing health impairment, and for which no predicate device exists. |
The majority of AI/ML-enabled SaMDs authorized to date have followed the 510(k) pathway [4]. This process requires demonstrating that the new device is substantially equivalent to a legally marketed predicate device. However, the unique nature of AI—especially its potential for continuous learning—strains this traditional model.
The complexity of these regulatory pathways, combined with the rapid pace of technological advancement, requires a deep understanding of both clinical medicine and regulatory science. For more in-depth analysis on this topic, the resources at www.rasitdinc.com provide expert commentary.
The Challenge of Adaptive AI: GMLP and the PCCP
The FDA recognizes that the traditional "locked" algorithm model—where the algorithm is fixed at the time of approval—is insufficient for modern, adaptive AI/ML systems that are designed to continuously learn and improve from real-world data. To address this, the FDA has proposed a new framework focused on the Total Product Lifecycle (TPLC) [5].
Key components of this evolving framework include:
- Predetermined Change Control Plan (PCCP): Manufacturers must submit a PCCP outlining the types of modifications they intend to make to the AI algorithm (e.g., performance improvements, new data sources) and the methods they will use to control and validate those changes. This allows for pre-specified changes to be implemented without requiring a new premarket submission each time.
- Good Machine Learning Practice (GMLP): The FDA, in collaboration with international partners, has established GMLP guiding principles. These principles emphasize data quality, model design, performance evaluation, and transparency to ensure the safety and effectiveness of AI/ML-enabled medical devices throughout their lifecycle [6].
This TPLC approach shifts the regulatory focus from a single point-in-time review to a continuous oversight model, ensuring that the benefits of adaptive AI are realized while maintaining patient safety.
The Future of AI Regulation: Transparency and Trust
The FDA's regulatory strategy for AI CDS is a dynamic blend of established medical device law and forward-thinking policy. By distinguishing between regulated SaMD and excluded CDS, and by developing frameworks like the PCCP and GMLP, the agency is striving to keep pace with innovation. The ultimate goal is to foster transparency, ensure the clinical validity of AI models, and build trust among healthcare professionals and the public in this powerful new generation of clinical tools [7]. The continuous evolution of this framework underscores the FDA's commitment to promoting public health in the age of digital medicine.
References
[1] FDA. Artificial Intelligence and Machine Learning in Software as a Medical Device (SaMD). https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device [2] Bipartisan Policy Center. FDA Oversight: Understanding the Regulation of Health AI Tools. https://bipartisanpolicy.org/issue-brief/fda-oversight-understanding-the-regulation-of-health-ai-tools/ [3] S. Gottlieb. New FDA Policies Could Limit the Full Value of AI in Clinical Decision Support. JAMA Health Forum, 2025. https://jamanetwork.com/journals/jama-health-forum/fullarticle/2830189 [4] G. Joshi et al. FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape. Electronics, 2024. [5] FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence and Machine Learning (AI/ML) - Based Software as a Medical Device (SaMD). https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf [6] FDA. Good Machine Learning Practice for Medical Device Development Guiding Principles. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles [7] V. Singh et al. United States Food and Drug Administration regulation of clinical software in the era of artificial intelligence and machine learning. Mayo Clinic Proceedings: Digital Health, 2025. https://www.sciencedirect.com/science/article/pii/S2949761225000380