How the FDA Reviews AI and Machine Learning Medical Devices: Complete 2025 Guide
- Beng Ee Lim
- Oct 6
- 7 min read
Updated: Oct 17
FDA reviews AI and machine learning medical devices using a regulatory framework designed for adaptive software that learns over time. The approach includes Predetermined Change Control Plans (PCCPs) that allow pre-approved algorithm modifications without additional submissions, Good Machine Learning Practice guidelines, and lifecycle management requirements addressing transparency and bias concerns.
This guide covers FDA's AI/ML regulatory framework, submission requirements, and strategies for successful device authorization in 2025.
Why AI/ML Medical Devices Need Different Regulatory Approaches
Traditional medical device regulations weren't designed for software that continuously learns and adapts. FDA's conventional paradigm assumes devices remain static after approval, but AI and machine learning algorithms improve performance by learning from real-world data.
The Core Challenge
Machine learning algorithms analyze data, identify patterns, and modify their behavior based on what they learn. A diagnostic AI might improve cancer detection accuracy after analyzing thousands of additional images. Under traditional regulations, each significant algorithmic change would require a new premarket submission.
FDA's Recognition
The agency recognizes that requiring separate submissions for every algorithm improvement would stifle innovation while creating regulatory bottlenecks. This realization led to development of specialized frameworks balancing continuous improvement with patient safety.
Understanding AI-Enabled Device Software Functions
FDA uses specific terminology to describe AI and machine learning in medical devices:
AI-Enabled Device Software Function (AI-DSF)
Any device software function using AI models to achieve its intended purpose. This includes both Software as a Medical Device (SaMD) and Software in a Medical Device (SiMD).
Machine Learning
A subset of AI using techniques to train algorithms that improve performance based on data. Most current FDA guidance focuses on ML-enabled devices since they represent the majority of AI submissions.
Adaptive AI
Software capable of learning from real-world use and modifying its performance without explicit reprogramming. This adaptive capability creates unique regulatory considerations.
FDA's AI/ML Regulatory Framework: Key Components
FDA has developed a multi-pronged approach to AI/ML medical device oversight:
Total Product Lifecycle (TPLC) Approach
AI/ML devices require oversight across their entire lifecycle, from initial development through post-market performance monitoring. This differs from traditional devices where post-market changes trigger new submissions.
Good Machine Learning Practice (GMLP)
FDA published guiding principles in October 2021 establishing best practices for ML medical device development. These principles emphasize robust data management, transparent model development, and continuous monitoring.
Predetermined Change Control Plans (PCCPs)
A revolutionary mechanism allowing manufacturers to specify planned algorithm modifications in their initial submission, then implement those changes without additional premarket review.
Risk-Based Oversight
FDA applies risk categorization principles from the International Medical Device Regulators Forum (IMDRF), focusing regulatory attention on devices with higher patient risk.
Predetermined Change Control Plans: The PCCP Framework
PCCPs represent FDA's most significant innovation for AI/ML device regulation, finalized in December 2024.
What Is a PCCP?
A PCCP is supplemental documentation included in a marketing submission (510(k), De Novo, or PMA) that describes planned AI modifications, implementation methodology, and impact assessment. Once FDA authorizes the PCCP, manufacturers can implement specified changes without new submissions.
Why PCCPs Matter
Without PCCPs, each significant algorithm modification would require a Special 510(k), PMA supplement, or new submission. For AI devices designed to learn continuously, this creates unsustainable regulatory burden. PCCPs enable iterative improvement while maintaining safety oversight.
PCCP Requirements
FDA requires three essential components in every PCCP:
Description of Modifications
Specific planned changes to the AI algorithm, including whether modifications will be automatic or require manual implementation. This section must detail the scope and boundaries of anticipated changes.
Modification Protocol
Methodology for developing, validating, and implementing modifications. This includes data management strategies, retraining procedures, performance evaluation methods, and update deployment processes.
Impact Assessment
Evaluation of risks and benefits associated with proposed changes, including risk mitigation strategies. Manufacturers must demonstrate how modifications maintain device safety and effectiveness.
PCCP Applicability and Limitations
Eligible Submission Types
PCCPs can be included in:
Original PMA applications
180-day PMA supplements
Traditional 510(k) submissions
De Novo classification requests
Important Exclusions
PCCPs are NOT permitted in Special 510(k) applications. Modifications outside PCCP scope still require traditional submissions.
Combination Products
PCCPs apply only to device components of device-led combination products, not drug or biologic components.
Staying Within PCCP Bounds
Manufacturers remain within authorized PCCP boundaries when they:
Only implement modifications contemplated by the PCCP
Deploy modifications exactly as specified in the protocol
Meet all specified acceptance criteria
Marketing Submission Requirements for AI-Enabled Devices
FDA issued draft guidance in January 2025 outlining comprehensive recommendations for AI device submissions.
Lifecycle Management Documentation
Submissions must address the entire product lifecycle, including:
Design and development processes
Data management strategies
Algorithm training and validation
Transparency Requirements
FDA emphasizes transparency in AI device design and operation. Submissions should explain how algorithms reach decisions and what data drives performance.
Bias Mitigation
Manufacturers must demonstrate consideration of diverse patient populations. FDA requires evidence that devices benefit all relevant demographic groups, including variations in age, sex, race, and ethnicity.
Cybersecurity Considerations
AI devices often process sensitive data and connect to networks. Submissions must address cybersecurity risks throughout the device lifecycle.
Good Machine Learning Practice Principles
FDA's GMLP guiding principles establish foundational practices for ML medical device development:
Data Management
Robust practices for data collection, labeling, storage, and version control. Data quality directly impacts algorithm performance and safety.
Feature Engineering
Transparent processes for selecting and engineering features that algorithms use for decision-making. Feature selection significantly influences model behavior.
Model Development
Systematic approaches to model training, including appropriate algorithm selection, hyperparameter tuning, and validation strategies.
Documentation and Transparency
Comprehensive documentation of design decisions, training data characteristics, model architecture, and performance metrics.
Performance Monitoring
Continuous monitoring of real-world performance to detect degradation, bias, or safety concerns that emerge post-market.
Pre-Submission Engagement Strategies
Q-Submission Process
FDA strongly encourages manufacturers to use the Q-Submission program before submitting PCCPs or AI device applications. Early engagement helps ensure alignment on:
Appropriateness of proposed modifications for PCCP inclusion
Required documentation and testing
Risk mitigation strategies
Performance metrics and acceptance criteria
Benefits of Early Engagement
Pre-submission meetings reduce delays by identifying potential issues before formal submission. FDA can provide feedback on novel AI approaches and help manufacturers understand current regulatory thinking.
Real-World AI/ML Device Examples
FDA maintains an AI-Enabled Medical Device List showing authorized products. Examples include:
Diagnostic Imaging AI
Algorithms providing diagnostic information from medical images, such as detecting diabetic retinopathy or identifying lung nodules in chest X-rays.
Predictive Analytics
Devices estimating patient risk for specific conditions, including heart attack prediction or sepsis early warning systems.
Clinical Decision Support
AI systems assisting healthcare providers with treatment decisions based on patient data analysis.
Post-Market Requirements for AI/ML Devices
Real-World Performance Monitoring
Manufacturers must monitor AI device performance in clinical use. This includes tracking accuracy metrics, identifying performance drift, and detecting unexpected behavior.
Adverse Event Reporting
Standard medical device reporting requirements apply to AI/ML devices. Manufacturers must report serious injuries or malfunctions through appropriate channels.
Labeling Updates
FDA recommends updating device labeling when modifications are implemented through PCCPs. Updates should inform users about changes and their implementation timing.
Quality System Requirements
AI/ML devices must comply with Quality Management System Regulation (QMSR) requirements, including design controls, risk management, and change control procedures.
Common Challenges and Solutions
Data Quality and Availability
Challenge: Obtaining sufficient high-quality training data representing diverse patient populations.
Solution: Establish robust data collection protocols early, consider multi-site collaborations, and document data limitations transparently.
Algorithm Validation
Challenge: Demonstrating algorithm performance across diverse clinical scenarios and patient populations.
Solution: Design comprehensive validation studies reflecting intended use conditions and patient diversity.
Regulatory Pathway Selection
Challenge: Determining appropriate regulatory pathway (510(k), De Novo, PMA) for novel AI devices.
Solution: Engage FDA early through pre-submission meetings to discuss device classification and pathway options.
PCCP Scope Definition
Challenge: Defining modification boundaries that enable innovation while maintaining safety.
Solution: Use Q-Submissions to obtain FDA feedback on proposed PCCP scope before formal submission.
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International Considerations
Global Harmonization Efforts
FDA collaborates with Health Canada and UK's MHRA on PCCP frameworks. While terminology and specific requirements vary internationally, core concepts are converging.
ISO Standards
International standards for software lifecycle processes and risk management apply to AI/ML devices. FDA guidance aligns with these standards where applicable.
Market-Specific Requirements
Companies pursuing global market access should consider how FDA's AI framework relates to requirements in other jurisdictions, particularly EU's Medical Device Regulation (MDR).
Future Regulatory Developments
Evolving Guidance
FDA continues developing AI/ML regulatory frameworks. Additional guidance documents addressing specific AI applications and technologies are expected.
Digital Health Center of Excellence
FDA's Digital Health Center of Excellence leads AI/ML regulatory policy development, coordinates agency activities, and engages with stakeholders.
Industry Standards Development
Consensus standards for AI/ML medical devices are evolving. FDA participates in standards development and recognizes applicable consensus standards.
Frequently Asked Questions
Do all AI medical devices need PCCPs?
No, PCCPs are optional but beneficial for devices designed to implement algorithmic modifications post-market. Devices without planned changes don't require PCCPs.
Can I modify my AI algorithm without a PCCP?
Yes, but modifications outside PCCP scope require traditional submissions (Special 510(k), PMA supplement, etc.) depending on the change's significance.
How long does FDA take to review AI device submissions?
Review timelines vary by submission type and complexity. Traditional review timelines for 510(k)s, De Novos, and PMAs generally apply to AI devices.
What if my algorithm performs differently than expected post-market?
Manufacturers must monitor real-world performance and report issues through appropriate channels. Significant performance problems may require corrective actions or regulatory submissions.
Do PCCPs work for combination products?
PCCPs generally apply only to device components of device-led combination products, not drug or biologic components.
How detailed should PCCP documentation be?
PCCPs should be detailed enough for FDA to assess safety and effectiveness of planned modifications while providing sufficient flexibility for implementation.