Executive Summary
Adoption of Artificial Intelligence technologies is accelerating across therapeutic product development, clinical operations, manufacturing, and quality systems—but the FDA is now signaling a far more assertive enforcement posture for AI deployed in regulated environments.
A recent AI-related warning letter revealed the agency’s expectations:
If AI informs labeling, performance claims, dosing, safety, or decision-making—then the entire solution must meet device-level quality, validation, and lifecycle controls.
This article explains:
- What changed in the FDA’s approach to AI
- Why both Quality/Regulatory and IT/Data leaders must act
- What the January 2025 FDA draft guidance really means
- How to prepare your AI systems, vendors, and teams for compliance
- How USDM can help operationalize AI safely and compliantly
The FDA Has Entered Its AI Enforcement Era
For years, AI innovation outpaced regulation. Many companies treated AI models—or vendor-supplied AI features—as “non-product” tools outside traditional validation expectations.
That era is over.
A defining moment: FDA’s warning letter to Exer Labs
The FDA issued a warning letter citing the company’s AI motion-analysis system used for musculoskeletal assessments. The agency classified the system as a medical device and cited deficiencies across:
- Design controls
- AI/ML model validation
- Data integrity
- Risk management
- CAPA, audit trails, and documentation
The takeaway was unmistakable:
When AI influences regulated decisions, the AI solution must meet full device-level requirements.
This is a direct signal to pharma, biotech, digital health, MedTech, and hybrid data-driven organizations. Below, I summarize what’s happening, why it matters, and how USDM is uniquely positioned to help companies get ahead of regulatory expectations.
The FDA’s New Enforcement Posture: AI Is a Regulated Technology
In April 2025, the FDA issued a warning letter to Exer Labs, citing misclassification of an AI-enabled diagnostic product, absence of required 510(k) clearance, and significant gaps in their Quality System (QS). Specific failures included:
- Missing design controls
- No CAPA procedures
- Insufficient audit trails
- Unqualified suppliers
- Training deficiencies
At its core, there were gaps in the quality management system The takeaway was unmistakable; when AI influences regulated decisions, the AI solution must meet full device-level requirements. This case demonstrates how quickly an AI application can cross the line into regulated territory and trigger full device-level expectations. Exer Labs attempted to bring to market a medical device with enhanced/diagnostic claims (AI-based screening, diagnosing, treating) without the regulatory foundation for that intended use (no pre-market clearance/approval) and without mature quality-systems practices required for regulated medical-device manufacture. In essence: the company scaled a novel use-case without establishing both regulatory compliance for the device’s intended claims and a robust quality-management system to support manufacturing and post-market controls.
Why the FDA’s Shift Matters Across the Organization
The implications extend beyond Compliance and Quality.
AI touches entire business processes—meaning compliance gaps can surface anywhere data flows.
For Quality & Regulatory (QA/RA):
- AI is now subject to design control rigor, not just CSA “scriptless” testing.
- Traceability, provenance, and explainability become compliance requirements.
- You must be able to show how AI outputs are verified, monitored, and controlled.
For IT, Digital, Data, and AI teams:
- Vendor-supplied AI features (e.g., “smart” modules) now have regulatory implications.
- Model lifecycle management, drift monitoring, and bias detection need discipline.
- Data pipelines feeding AI must meet GxP integrity and transparency standards.
For Executives:
- AI risk is now business risk.
- The FDA expects governance systems, not experiments.
- AI investments need compliance readiness baked in from the start.
Inside the FDA’s January 2025 Draft Guidance on AI
The 2025 draft guidance marks the FDA’s strongest effort to date to define expectations for AI used in:
- Clinical trial analysis
- Therapeutic product development
- Digital health tools
- Manufacturing
- Quality systems
- Post-market safety monitoring
Key expectations include:
1. Context-Specific Validation
Validation must reflect intended use, training data, and real-world operating conditions.
2. Model Transparency & Explainability
Organizations must document:
- What data trained the model
- How features were selected
- The model’s decision logic (to the extent possible)
3. Data Integrity & Governance
AI must comply with ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available), including:
- Access control
- Immutable audit trails
- Versioning
- Data lineage
4. Bias Mitigation Requirements
Models must demonstrate:
- Fairness assessments
- Bias detection
- Corrective measures
- Ongoing monitoring
5. Continuous Performance Monitoring
AI is never validated once. The FDA expects ongoing lifecycle evaluation, including:
- Drift monitoring
- Retraining controls
- Change management
This bridges FDA expectations with leading frameworks like GMLP, ICH E6(R3), ICH Q9, and NIST AI RMF.
What Life-Science Companies Must Do Now
Here’s what your organization should prioritize within the next 6–12 months.
1. Establish AI Governance & Accountability
Move from experimentation to an operating model:
- AI governance board
- Responsible AI principles
- Risk classification
- Vendor oversight
- Clear ownership across QA, IT, and business teams
2. Classify All AI Systems According to Risk
Build (or adapt) your AI inventory to classify systems as:
- High-risk (decision support, patient safety, QC inspection, deviation management)
- Medium-risk (forecasting, operations optimization)
- Low-risk (productivity, reporting)
Tie controls to risk—not hype.
3. Qualify Vendors and Third-Party AI Features
Most AI solutions your teams touch will come from vendors.
The FDA expects:
- Vendor audits
- Security and bias controls
- Architecture transparency
- Validation and model documentation
- Clear change-control procedures
This is where organizations are currently least prepared.
4. Strengthen Validation, Data Integrity, and Traceability Controls
Validation of AI looks more like validation of analytics, modeling, and decision engines—not simple feature/function testing.
Key elements include:
- Model evaluation protocol (accuracy, sensitivity, drift thresholds)
- Data lineage and traceability
- Performance monitoring plans
- Training/validation/test data documentation
- Explainability and bias testing
5. Prepare for FDA Questions Before They Come
When auditors see AI outputs influencing regulated decisions, they will ask:
- “How do you know the model is performing correctly today?”
- “How do you detect drift?”
- “What controls prevent unintended behavior?”
- “Who approved the last retraining cycle?”
- “Can you trace this output back to an auditable input?”
Your teams need answers ready—now.
AI in GxP: 10-Point Readiness Checklist
- Inventory all AI systems and classify by risk.
- Identify AI features embedded in vendor tools.
- Establish cross-functional AI governance roles.
- Document intended use, data sources, and training data.
- Create model validation & monitoring procedures.
- Implement bias detection and mitigation controls.
- Ensure traceability, versioning, and immutable audit trails.
- Map data lineage from raw input to model output.
- Qualify vendors and require transparency documentation.
- Create a change-control and post-release monitoring plan.
How USDM Helps Organizations Operationalize AI Safely
USDM brings 25+ years of life-sciences compliance expertise combined with deep AI technical understanding to help you move from experimentation to enterprise-grade AI.
Our support includes:
- AI Assessment & Readiness Review
- AI Assurance Framework
- AI Vendor Qualification & Third-Party Risk Assessment
- CSA/CSV for AI-enabled systems
- Model lifecycle validation & monitoring controls
- Data integrity, lineage, and traceability architecture
- AI governance framework design
- Documentation packages for audits & inspections
- Data Architecture & Strategy
- Data Ingestion and Pipelines
AI does not relax compliance requirements—it amplifies the need for transparency, governance, and control.
What Happens Next: The AI Compliance Landscape Will Keep Accelerating
Expect further FDA guidance on:
- Adaptive AI (continually learning models)
- SaMD and clinical decision support
- AI in manufacturing analytics and real-time release
- AI-driven quality management systems
- AI transparency standards
Preparing now puts your organization ahead—not just in compliance, but in AI-enabled innovation.
Act Now
If your organization is deploying or evaluating AI, now is the time to ensure you’re prepared for FDA expectations. Contact USDM to schedule your AI Readiness Assessment and build a clear, compliant path to safe, scalable AI adoption.
Additional Resources
Read our white paper on Anticipating Regulatory Compliance for Artificial Intelligence in Life Sciences or check out these AI case studies to learn more.
- Complaint Processing Enhanced by AI in Medical Device Manufacturing
- Streamlining Clinical Trials with the LLM Protocol Assistant
- Leveraging AI for Enhanced Clinical Trial Data Management in Life Sciences
- Intelligent Query Monitoring Helps Identify High- and Low-Performing Clinical Sites and Users
Frequently Asked Questions
Does the FDA regulate AI systems even if they’re not sold as medical devices?
Yes. If AI influences GxP decisions—manufacturing, labeling, safety, QC, batch release, or clinical data interpretation—the FDA may consider it subject to device-level controls.
Are AI features inside vendor tools subject to FDA expectations?
Absolutely. “Smart” features in QMS, MES, LIMS, CTMS, and statistical analysis tools must be validated and governed.
Do we need to monitor AI models after deployment?
Yes. AI validation is continuous. You must track performance, detect drift, and document retraining.
What documentation does the FDA expect for AI?
Design controls, data lineage, intended use, validation strategy, bias mitigation, monitoring plans, change control, and audit trails.
