FDA Regulatory Framework for AI/ML Devices
The U.S. Food and Drug Administration (FDA) regulates AI/ML-enabled medical devices under the Federal Food, Drug, and Cosmetic Act (FD&C Act). Unlike traditional software that follows deterministic logic, AI/ML algorithms can learn from data and evolve. That evolution presents unique regulatory challenges, which FDA has addressed through a series of guidance documents and policy frameworks.[1][2]
Evolution of FDA’s AI/ML Approach
FDA’s journey toward comprehensive AI/ML regulation began with the 2019 discussion paper “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device.” This established the conceptual foundation for what would become the Total Product Lifecycle (TPLC) approach. TPLC is a regulatory model that acknowledges AI/ML devices may change over time while maintaining safety and effectiveness.[6]
Key milestones in FDA’s AI/ML regulatory evolution:
- 2019: Proposed regulatory framework discussion paper establishing TPLC concept
- 2021: AI/ML-Based SaMD Action Plan outlining five priority areas
- 2021: Good Machine Learning Practice (GMLP) guiding principles with Health Canada and UK MHRA
- 2023: Draft PCCP guidance for public comment
- December 2024: FDA originally issued final PCCP guidance for AI-enabled device software functions
- January 2025: AI-enabled device software lifecycle guidance (DRAFT)
- August 18, 2025: FDA reissued the same final guidance; the current document identifies both the original and reissue dates[1]
Scope of FDA Oversight
FDA regulates AI/ML-enabled devices that meet the definition of a “device” under the FD&C Act. That definition covers products intended for use in diagnosis, cure, mitigation, treatment, or prevention of disease. This includes:
- Software as a Medical Device (SaMD): standalone software performing medical functions
- Software in a Medical Device (SiMD): software integral to a hardware device
- AI-enabled diagnostic systems: imaging analysis, pathology, radiology
- Clinical decision support: systems that inform, but don’t replace, clinical judgment
Software as a Medical Device (SaMD) Classification
IMDRF publishes a nonbinding SaMD risk-categorization framework based on the significance of the information provided and the healthcare situation or condition. It is useful context, but FDA classification and submission requirements arise from U.S. law, intended use, device risk, and the applicable FDA pathway.[7]
IMDRF Risk Categorization Matrix
SaMD Risk Categories
| State of Healthcare Situation | Treat or Diagnose | Drive Clinical Management | Inform Clinical Management |
|---|---|---|---|
| Critical | IV (Highest) | III | II |
| Serious | III | II | I (Lowest) |
| Non-Serious | II | I | I |
FDA’s public AI-enabled-device list is heavily weighted toward radiology and 510(k) authorizations, but FDA says the list is not comprehensive and it changes over time. Classification and submission requirements must be determined from the device’s intended use, risk, applicable classification regulation, predicates where relevant, and current FDA pathway.[4]
Device Classification Examples
Class I (Low Risk)
- • General wellness applications
- • Administrative workflow tools
- • Non-clinical decision support
Generally exempt from premarket submission
Class II (Moderate Risk)
- • Radiology AI (CADe/CADx)
- • ECG analysis software
- • Diabetic retinopathy screening
510(k) or De Novo pathway required
Class III (High Risk)
- • Autonomous diagnostic systems
- • AI-driven treatment recommendations
- • Life-sustaining device algorithms
PMA with clinical trials required
Clinical Decision Support (CDS) Exemptions
- • Basis for recommendation is transparent
- • Provider can independently review basis
- • Not intended to replace clinical judgment
May be exempt per 21st Century Cures Act
Predetermined Change Control Plans (PCCP)
FDA issued final PCCP guidance tailored to AI-enabled device software functions on August 18, 2025. A PCCP included in a 510(k), De Novo, or PMA submission can describe specified future modifications, the methods for developing, validating, and implementing them, and an impact assessment. Once FDA authorizes that PCCP as part of the device submission, changes within its scope can proceed without a separate marketing submission for each modification. The guidance states FDA’s current, nonbinding recommendations; it did not establish a separate “full implementation” date.[1]
PCCP Requirements
A PCCP submitted as part of a marketing application must include:
PCCP Core Components
| Component | Description | FDA Expectation |
|---|---|---|
| Description of Modifications | Specific types of changes that may be made to the device | Clear boundaries on what changes are pre-authorized |
| Modification Protocol | Methodology for developing and implementing changes | Documented development process with quality controls |
| Impact Assessment | Methods to evaluate the effect of modifications on safety/effectiveness | Quantitative metrics and acceptance criteria |
| Verification and Validation | Testing protocols for each type of modification | Evidence that changes meet performance specifications |
| Traceability | Documentation linking modifications to assessments | Documented traceability between in-scope modifications, assessments, and verification results |
Modifications Requiring New Submissions
Even with an approved PCCP, certain modifications fall outside its scope and require new marketing submissions:
- Changes to intended use: expanding indications, new patient populations
- New input types: different imaging modalities, data sources
- Fundamental algorithm changes: new ML architectures, training approaches
- Performance degradation: changes that reduce safety or effectiveness
- New risks: modifications introducing hazards not previously addressed
PCCP Documentation Best Practices
Organizations implementing PCCPs should maintain:
- Version control: governed records for in-scope model versions and declared deployment timestamps, with retention and access controls matched to the change-management process
- Validation datasets: curated test sets used to verify each modification
- Performance baselines: documented metrics against which changes are evaluated
- Decision logs: rationale for determining whether changes fall within PCCP scope
Submission Pathways for AI/ML Devices
Common premarket routes for AI/ML medical devices include 510(k), De Novo, and PMA. Other statutory pathways, exemptions, and postmarket requirements may apply; the correct route depends on the specific device and current FDA framework.[5]
510(k) Premarket Notification
The 510(k) pathway is used when a device is substantially equivalent to a legally marketed predicate device. Many entries in FDA’s public AI-enabled-device list use this route, but that evolving, non-comprehensive list should not be turned into a universal pathway percentage.
510(k) Key Requirements
- • Identify legally marketed predicate device
- • Demonstrate same intended use and similar technological characteristics
- • Performance testing comparing device to predicate
- • Software documentation per FDA guidance
Timing: FDA review clocks, interactive review, requests for additional information, and total elapsed time vary by submission and pathway. Confirm the current statutory and performance-goal framework for the specific device.
De Novo Classification
The De Novo pathway is for novel, low-to-moderate risk devices without a predicate. Many innovative AI/ML devices use this pathway when no substantially equivalent device exists.
De Novo Key Requirements
- • Demonstrate device is low-to-moderate risk
- • General and special controls adequate to provide reasonable assurance of safety and effectiveness
- • Propose device classification and product code
- • Clinical or analytical validation data
Timing: statutory review goals and actual elapsed time depend on the pathway, submission quality, interactive review, requests for information, and sponsor response. Confirm the current system-specific plan with FDA and regulatory counsel.
Premarket Approval (PMA)
PMA is required for Class III devices that pose the highest risk. This pathway requires the most extensive clinical evidence and FDA review.
PMA Key Requirements
- • Valid scientific evidence demonstrating safety and effectiveness
- • Clinical trials typically required
- • Manufacturing quality systems inspection
- • Post-market surveillance commitments
Timing: The PMA review clock is 180 days after filing; actual elapsed time varies with filing completeness, interactive review, requests for information, inspections, and sponsor response.
Total Product Lifecycle (TPLC) Approach
FDA’s Total Product Lifecycle approach recognizes that AI/ML devices are fundamentally different from traditional medical devices. They’re designed to learn, adapt, and improve. The January 2025 AI-enabled device software lifecycle draft guidance proposes expectations for managing these devices throughout their entire lifecycle.[2]
TPLC Core Principles
Good Machine Learning Practice (GMLP)
FDA, Health Canada, and UK MHRA jointly published 10 guiding principles for GMLP covering data quality, model design, performance evaluation, and ongoing monitoring. These principles form the foundation of lifecycle management expectations.
Algorithm Change Protocol
Manufacturers must establish protocols for how algorithm changes are developed, validated, and deployed. This includes defining what constitutes a “significant” change requiring new submission versus a change manageable under a PCCP.
Performance Monitoring Strategy
Continuous monitoring of real-world performance is expected, including detection of performance drift, monitoring across subpopulations, and processes for addressing performance degradation.
Re-Training Protocols
For adaptive algorithms, manufacturers must document how re-training decisions are made, what data is used, how validation is performed, and how updates are deployed while maintaining device safety.
Locked vs. Adaptive Algorithms
FDA distinguishes between two fundamental types of AI/ML algorithms:
Locked Algorithms
Algorithm produces same result each time same input is applied. Does not change after deployment.
- → Traditional regulatory pathway applies
- → Changes require new submission or PCCP
- → Simpler post-market monitoring
Adaptive Algorithms
Algorithm changes its behavior over time based on new data or learning from deployed use.
- → PCCP strongly recommended
- → Continuous monitoring required
- → Re-training protocols must be documented
Cybersecurity Requirements for AI Devices
FDA’s February 2026 Cybersecurity in Medical Devices guidance provides recommendations for devices with cybersecurity risk and supersedes the June 2025 final guidance. It recommends risk-based cybersecurity design and premarket documentation, including threat modeling, security-risk management, vulnerability handling, and update planning. It does not apply an identical control set or universal cryptographic requirement to every software device.[3]
Core Cybersecurity Requirements
Cybersecurity Submission Elements
| Requirement | Description | AI-Specific Considerations |
|---|---|---|
| Threat Modeling | Identification and analysis of potential cybersecurity threats | Adversarial inputs, model poisoning, data manipulation |
| Security Risk Assessment | Evaluation of exploitability and severity of identified threats | Model extraction, inference attacks, training data leakage |
| Security Controls | Technical measures to mitigate identified risks | Input validation, anomaly detection, model integrity checks |
| Software Bill of Materials | Inventory of all software components including ML libraries | Model dependencies, training frameworks, inference engines |
| Vulnerability Management | Processes for identifying and addressing vulnerabilities | Model vulnerability scanning, adversarial testing |
AI-Specific Security Concerns
AI/ML devices face unique cybersecurity threats beyond traditional software:
- Adversarial attacks: carefully crafted inputs designed to cause misclassification
- Data poisoning: manipulation of training data to introduce vulnerabilities
- Model extraction: unauthorized copying of proprietary algorithms
- Membership inference: attacks revealing training data contents
Real-World Performance Monitoring
FDA increasingly emphasizes the importance of monitoring AI/ML device performance in real-world clinical settings. The September 2025 request for public comment on AI device performance underscores the agency’s focus on developing standardized approaches to real-world evidence collection.[8]
Monitoring Expectations
Manufacturers should implement monitoring systems that track:
- Algorithm accuracy: ongoing performance metrics compared to validation benchmarks
- Performance drift: detection of degradation over time or across changing data distributions
- Subpopulation performance: monitoring across demographic groups, clinical sites, and use cases
- Unexpected outputs: logging and analysis of edge cases and anomalies
- User feedback: mechanisms to capture clinician reports of errors or concerns
Evidence Infrastructure Requirements
Effective real-world monitoring requires infrastructure capable of:
Per-Inference Logging
For covered events, preserve purpose-appropriate fields or references for inputs, outputs, declared model version, and what configured guardrails reported. Decide separately when full payload retention is lawful and necessary.
Tamper-Evident Audit Records
Logs should be tamper-evident and independently verifiable. Regulatory submissions may require demonstrating that records haven’t been altered.
Automated Alerting
Systems should automatically detect performance degradation and alert appropriate personnel when metrics fall outside acceptable ranges.
Reporting Capabilities
Generate reports suitable for regulatory submissions, including MDR (Medical Device Report) documentation when adverse events occur.
Frequently Asked Questions
What is a Predetermined Change Control Plan (PCCP)?
A PCCP is reviewed by FDA as part of a marketing submission and can cover specified future modifications to an AI-enabled device software function. FDA issued its final AI-enabled device PCCP guidance on August 18, 2025. The guidance recommends describing the planned modifications, the methods for developing, validating, and implementing them, and an impact assessment. It contains nonbinding recommendations; it did not create a separate August 2025 compliance deadline.
What are the FDA submission pathways for AI/ML medical devices?
AI/ML medical devices can be cleared or approved through three main pathways: 510(k) Premarket Notification for devices substantially equivalent to predicate devices, De Novo Classification for novel low-to-moderate risk devices without predicates, and Premarket Approval (PMA) for high-risk Class III devices. The pathway depends on device classification, risk level, and whether a suitable predicate exists.
What is SaMD and how does FDA regulate it?
Software as a Medical Device (SaMD) is software intended for one or more medical purposes that performs those purposes without being part of a hardware medical device. IMDRF publishes a nonbinding risk-categorization framework, while FDA classification and submission requirements depend on U.S. law, intended use, device risk, and the applicable pathway.
What cybersecurity requirements apply to AI medical devices?
FDA’s February 2026 cybersecurity guidance provides nonbinding recommendations for devices with cybersecurity risk. Recommended premarket material includes threat modeling, security-risk assessment, vulnerability management, and update planning. Select controls from the device’s architecture and risk analysis; neither the guidance nor AI functionality creates a universal cryptographic-control checklist.
What is the Total Product Lifecycle (TPLC) approach for AI devices?
The Total Product Lifecycle (TPLC) approach is FDA’s framework for regulating AI/ML devices throughout their entire lifecycle, from development through post-market surveillance. It emphasizes continuous learning, real-world performance monitoring, and iterative improvement while maintaining safety and effectiveness. The TPLC approach supports the Good Machine Learning Practice (GMLP) principles and enables PCCPs for controlled algorithm updates.
How does FDA define locked vs. adaptive AI algorithms?
Locked functions do not change their behavior after deployment; adaptive functions may change through a defined update process. An FDA-authorized PCCP can cover specified planned modifications, but a PCCP is not mandatory for every adaptive function or every change. Whether a modification needs a new submission depends on the authorized device, applicable guidance, the change, and any authorized PCCP. The January 2025 lifecycle document remains draft, non-binding guidance.
What real-world performance monitoring does FDA expect for AI devices?
FDA expects manufacturers to monitor AI/ML device performance in real-world clinical settings through post-market surveillance. This includes tracking algorithm accuracy, detecting performance drift, monitoring for unexpected outputs, and assessing performance across different patient populations. The September 2025 request for public comment signals FDA’s focus on standardized approaches to real-world evidence collection for AI devices.
When do AI device changes require new FDA submissions?
AI device changes require new FDA submissions when they: (1) affect safety or effectiveness beyond what was originally authorized, (2) fall outside an approved PCCP, (3) change the intended use, or (4) introduce new risks. Minor changes within an approved PCCP may proceed without new submissions, but manufacturers must document all modifications and maintain evidence that changes meet PCCP criteria.
References
- [1] U.S. Food and Drug Administration. “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions.” Final guidance, August 18, 2025. fda.gov
- [2] U.S. Food and Drug Administration. “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations.” Draft Guidance, January 6, 2025. fda.gov
- [3] U.S. Food and Drug Administration. “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.” Final guidance, February 2026; supersedes the June 2025 final guidance. fda.gov
- [4] U.S. Food and Drug Administration. “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices.” Updated 2025. fda.gov
- [5] U.S. Food and Drug Administration. “Premarket Submission for Device Software Functions.” November 2021. fda.gov
- [6] U.S. Food and Drug Administration. “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device.” Discussion Paper, 2019; indexed in FDA’s AI publications collection. fda.gov
- [7] International Medical Device Regulators Forum (IMDRF). “Software as a Medical Device (SaMD): Possible Framework for Risk Categorization and Corresponding Considerations.” 2014. imdrf.org
- [8] U.S. Food and Drug Administration. “Request for Public Comment: Measuring and Evaluating Artificial Intelligence-Enabled Medical Device Performance in the Real-World.” September 30, 2025. fda.gov
- [9] FDA, Health Canada, UK MHRA. “Good Machine Learning Practice for Medical Device Development: Guiding Principles.” October 2021. fda.gov
- [10] U.S. Food and Drug Administration. “FDA Seeks Public Feedback to Inform Regulatory Approach for Generative AI-Enabled Medical Devices.” Discussion paper announcement, 18 August 2026. fda.gov
- [11] U.S. Food and Drug Administration. “Digital Health Center of Excellence.” fda.gov