AI & Machine
Learning Compliance
The algorithm is not just your product. It is your evidence. We help life sciences organizations navigate FDA's evolving AI/ML regulatory framework before it creates liability.
When software makes clinical decisions, the regulator examines the logic, not just the output.
FDA's approach to AI/ML is fundamentally different from traditional software regulation. The agency now expects sponsors to treat the algorithm's lifecycle as a continuous regulatory obligation, not a one-time validation exercise cleared with a 510(k) and forgotten.
Organizations that approach AI/ML compliance the way they approach legacy SaMD validation find out the difference when a Warning Letter arrives or a premarket submission is placed on hold.
For AI/ML-based SaMD, FDA evaluates training methodology, validation datasets, and performance specifications with the same scrutiny as a 510(k) predicate comparison, often more. The model's behavior in edge cases is now a regulatory question.
Algorithm modifications after clearance, even to improve performance, can constitute a new device requiring a new submission. Without a Predetermined Change Control Plan filed and cleared by FDA, sponsors often learn this only after making the change.
FDA's Digital Health Center of Excellence, Good Machine Learning Practice principles, and PCCP guidance are producing new expectations faster than most compliance teams can absorb. A cleared device may not meet the standard against which it will be compared in the next review cycle.
Bring your AI/ML program to a senior regulatory partner before the submission, not after the Complete Response Letter.
Schedule a Strategy CallThe Full Lifecycle. Algorithm Design Through Post-Market Surveillance.
We do not treat AI/ML compliance as a checklist appended to a 510(k). Every engagement starts from where your algorithm sits in its lifecycle and builds a regulatory strategy outward from that point.
AI/ML SaMD Regulatory Strategy & Pathway Selection
Choosing the right regulatory pathway for an AI/ML-based device (De Novo, 510(k), or PMA) requires understanding both the predicate landscape and the agency's current review posture on the specific algorithm type. We map your AI/ML system against FDA's SaMD risk framework, identify predicate strategies, and structure your technical file to support the Total Product Lifecycle approach FDA now applies to adaptive systems.
Predetermined Change Control Plans (PCCPs)
A PCCP filed at clearance gives sponsors a defined corridor for post-market algorithm updates without triggering a new submission for every modification. Writing a PCCP FDA accepts requires deep familiarity with what the agency has reviewed to date: it must be specific enough to be binding, yet broad enough to allow meaningful improvement. We draft, negotiate, and file PCCPs across diagnostic, therapeutic, and monitoring AI/ML applications.
Good Machine Learning Practice (GMLP) Implementation
The joint GMLP principles published by FDA, Health Canada, and MHRA in 2023 define the floor for how regulators globally expect ML-based devices to be designed, trained, and validated. We translate these principles into operational quality system documentation: SOPs, design controls, data governance frameworks, and model governance structures, each aligned to satisfy multiple agency requirements in parallel.
Algorithm Validation, Testing & Bias Assessment
Validating an ML model for regulatory submission differs from standard software V&V. Test set independence, subgroup performance analysis, distributional shift assessment, and explainability requirements each introduce technical validation demands that traditional IQ/OQ/PQ frameworks do not address. We design validation protocols for diagnostic, therapeutic, and prognostic AI that satisfy FDA technical review standards and pre-empt the most common objections in AI/ML reviews.
Post-Market AI/ML Surveillance & Performance Monitoring
AI/ML devices cleared under a PCCP, or any adaptive algorithm operating in real-world conditions, require active performance monitoring programs that detect model drift before clinical impact occurs. We design post-market surveillance architectures for AI systems: real-world data collection pipelines, statistical process control for model performance, adverse event evaluation for algorithm-driven errors, and MAUDE reporting protocols for AI-related events.
AI/ML in Drug Development & Pharmacovigilance
AI is not only a medical device regulatory question. It reaches into drug development: FDA-qualified biomarkers derived from AI models, AI-assisted clinical trial design under decentralized trial frameworks, digital endpoints, and AI-driven pharmacovigilance signal detection each carry distinct regulatory obligations under CDER and CBER oversight. We navigate these requirements across modalities, from AI-based diagnostic companion tests to ML-powered safety surveillance systems.
The Right Pathway Is Not Always the Obvious One.
FDA's SaMD risk framework classifies AI/ML devices by the significance of the information provided and the healthcare situation in which it is used. But pathway selection for AI, particularly between De Novo and 510(k), also turns on predicate specificity and the stability of the algorithm architecture. A device that would be a straightforward 510(k) without AI may require De Novo when the AI component changes the clinical output in ways the predicate does not support.
We conduct predicate landscape analyses for AI/ML applications across radiology, pathology, cardiology, ophthalmology, and continuous monitoring, and advise on when a Pre-Submission to the Digital Health Center of Excellence is the right first step versus a direct submission. We have seen what FDA accepts for specific algorithm types and where reviews stall; we build submissions to avoid the latter.
Pathway Assessment Covers
- SaMD risk level and intended use analysis
- Predicate identification and gap mapping
- De Novo vs. 510(k) decision framework
- PMA threshold evaluation for high-risk AI
- DHCoE Pre-Submission preparation
- CDRH division routing strategy
- Regulatory history review for algorithm type
- International pathway alignment (EMA, MHRA, PMDA)
The Change You Need Tomorrow Requires the Filing You Make Today.
FDA's 2024 final guidance on Predetermined Change Control Plans established a formal mechanism for sponsors to describe, in advance, the types of algorithm modifications they intend to make post-market and the performance testing that will validate each change. A cleared PCCP is, in practice, a regulatory license to improve your algorithm under defined conditions without triggering additional premarket review.
Writing a PCCP FDA accepts is not a documentation exercise. It requires predicting which modifications will be meaningful over the device's commercial life, defining performance thresholds that are clinically relevant and statistically rigorous, and specifying change protocols specific enough that FDA can evaluate their adequacy in advance. We have written PCCPs across imaging AI, clinical decision support, and continuous monitoring systems and have negotiated the agency's questions on plan specificity, testing adequacy, and subgroup monitoring requirements.
PCCP Development Covers
- Algorithm change protocol design
- Performance metric specification and thresholds
- Subgroup monitoring requirements
- Testing methodology for prospective changes
- Real-world data collection requirements
- PCCP narrative for IDE/510(k)/De Novo inclusion
- Q-sub preparation and negotiation
- Implementation and change execution oversight
International Regulators Are Now Reading Your Training Documentation.
The 2023 update to the joint FDA/Health Canada/MHRA Good Machine Learning Practice principles extended regulatory expectations to encompass data management, model design, testing and evaluation methods, human factors in AI-assisted decisions, and ongoing monitoring requirements. These principles are now referenced in EU MDR technical file expectations for AI/ML devices and are influencing PMDA's emerging AI framework in Japan.
GMLP implementation is not a single document. It is a quality system architecture. We assess your existing QMS against GMLP requirements and design the additions: model governance procedures, training data traceability controls, validation independence requirements, bias evaluation protocols, and the audit trail structure that regulatory inspectors will look for when they examine an AI/ML device file.
GMLP Program Development
- Gap assessment against joint GMLP principles
- Training and test data documentation framework
- Model versioning and governance SOPs
- Validation independence requirements
- Bias and equity evaluation protocol
- Transparency and explainability documentation
- Human factors for AI-assisted decisions
- QMS integration and design control mapping
Five Years of FDA AI/ML Policy. One Accelerating Standard.
FDA has moved from a discussion paper to an active regulatory framework faster than most organizations have adapted. Understanding where each guidance sits in the hierarchy, and which elements carry premarket review weight versus post-market inspection weight, is foundational to strategy.
FDA proposes a Total Product Lifecycle (TPLC) regulatory approach for AI/ML software as a medical device, recognizing that adaptive algorithms require ongoing oversight beyond initial premarket review.
FDA formalizes a five-pillar action plan: good machine learning practice, algorithm transparency, representative datasets, real-world performance monitoring, and regulatory science tools. The framework becomes the template for subsequent guidance.
CDRH formally establishes the DHCoE to build regulatory science and provide a coordinated review point for digital health and AI/ML submissions across device categories.
FDA, Health Canada, and MHRA jointly publish GMLP principles covering data management, model design, validation, and monitoring. The international alignment signals that submissions must satisfy multiple regulatory bodies simultaneously.
FDA releases draft guidance establishing the formal framework for Predetermined Change Control Plans: the mechanism allowing cleared devices to implement defined algorithm modifications without new premarket submissions.
FDA finalizes PCCP guidance and extends AI oversight into drug development, pharmacovigilance, and decentralized clinical trials under CDER and CBER jurisdiction. AI compliance is no longer exclusively a device question.
Regulators are examining AI/ML systems in premarket submissions, post-market inspections, and pharmacovigilance audits against the full body of GMLP, PCCP, and TPLC expectations. Organizations without mature AI compliance programs face increasing submission delays and inspection findings.
Regulatory Practitioners. Not AI Vendors.
Our AI/ML compliance work is done by senior regulatory professionals who have managed SaMD submissions at FDA and in industry, not by software engineers or data scientists who have pivoted into regulatory consulting. The distinction matters when submissions are under review.
SaMD Submission Experience
Practitioners with direct experience managing 510(k), De Novo, and PMA submissions for software-only and AI/ML-driven devices across diagnostic imaging, clinical decision support, and continuous patient monitoring applications.
CDRH Division Relationships
We track review posture by CDRH division and product code for AI/ML applications. Where you submit and how you frame the algorithm matters; we bring the pattern recognition that comes from following cleared submissions across the landscape.
Quality System Depth
AI/ML compliance failures often live in the quality system, not the submission. We design QMS extensions (model governance, design control integration, training data traceability) that satisfy both FDA 21 CFR 820 and ISO 13485 requirements simultaneously.
Global Regulatory Alignment
An AI/ML device cleared in the U.S. faces separate, and not always harmonized, requirements in Europe (EU MDR AI Act obligations), Japan (PMDA), and Canada (Health Canada SaMD framework). We plan for all markets from the initial regulatory strategy.
Regulatory Frameworks We Navigate
- FDA AI/ML SaMD Action Plan
- GMLP Joint Principles
- 21 CFR Part 820 QSR
- 21 CFR Part 11
- ISO 13485:2016
- EU MDR 2017/745
- EU AI Act
- IEC 62304
- IMDRF SaMD Framework
- PMDA AI Guidance
- Health Canada SaMD
Your AI/ML Regulatory Strategy Starts With a Conversation.
Tell us where your algorithm is in its lifecycle (development, pre-submission, cleared, or post-market) and we'll identify the highest-risk regulatory gaps and the fastest path to address them. All inquiries are strictly confidential.