Guidance for Life Sciences Leaders
The FDA has sent its clearest message yet: AI deployed in GxP environments is now firmly in the agency’s enforcement crosshairs. Over the past year, we’ve seen a dramatic uptick in warning letters, draft guidance, and public statements underscoring that AI—whether embedded in medical devices, used for clinical decision-making, or powering internal GxP workflows—must meet the same standards of validation, transparency, and control as any other regulated technology.
This shift is not theoretical. The FDA has already taken action, and it has major implications for life sciences organizations that are experimenting with or scaling AI.
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 verses AI-specific infringement, but the 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.
Draft Guidance on AI in Drug Development
FDA’s January 2025 draft guidance outlines a risk-based credibility framework focused on:
- Context-specific validation
- Bias mitigation
- Transparency and traceability
- Early engagement with the agency
This guidance establishes baseline expectations for any AI used in GxP drug development processes.
Data Integrity Crackdowns
FDA inspections continue to find issues related to:
- Inadequate audit trails
- Lack of ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available) and compliance
- Unvalidated AI tools used in documentation or decision-making
AI does not relax data integrity rules—it heightens them.
FDA Using AI Internally—But Industry Still Must Comply
While FDA scales its own AI tools (e.g., Elsa), federal and state-level regulation of AI for industry continues to evolve. The regulatory gap means companies must comply with existing GxP frameworks even as the AI landscape changes around them.
What This Means for Life Sciences Teams
Organizations experimenting with generative AI, deploying AI in clinical workflows, or integrating machine learning into digital health products must ensure:
- Validated models and processes
- Clear documentation and design controls
- Traceable data lineage
- Qualified suppliers and vendors
- Robust governance and monitoring mechanisms
Most companies are still in what we call Phase 1 (Learn)—experimenting with proofs of concept and early use cases. But FDA’s posture now requires a shift into Phase 2 (Control), where governance, risk mitigation, and validation are mandatory.
[Download USDM’s AI Assurance datasheet]
How USDM Helps: AI Assessment + Third-Party Risk Assessment
USDM has developed a purpose-built AI Assurance Framework designed specifically for regulated life sciences organizations. This is not a generic technology model—it is a GxP-aligned approach that integrates validation, compliance, and cross-functional governance.
1. Proprietary USDM AI Assessment
Your journey to compliant AI begins with our comprehensive AI Assessment, which evaluates:
- AI governance
- Data management
- People and skill readiness
- GxP and non-GxP use cases
- Cybersecurity
- Infrastructure and tooling
- Validation requirements
The assessment identifies areas of regulatory risk and provides a roadmap to safely operationalize AI across the enterprise that uncovers opportunities, compliance gaps, and organizational needs.
2. AI Assurance Framework: Learn → Control → Expand
USDM’s model helps organizations move from experimentation to full-scale adoption:
- Phase 1: Learn
- Controlled experimentation with prompt templates and citizen development
- Early discovery and rapid wins
- Phase 2: Control
- Governance models
- Policies, SOPs, work instructions
- Continuous monitoring, training, QA/QC
- Validation workflows (data acquisition → model validation)
- Phase 3: Expand
- Scaled enterprise use
- Centers of Excellence
- Continuous improvement and innovation pathways
3. Third-Party Risk Assessment for AI Vendors
FDA scrutiny extends beyond your internal systems—vendors, models, and AI-enabled tools must also meet GxP and data integrity standards.
USDM helps organizations:
- Classify AI tools (GxP vs non-GxP)
- Assess model transparency and explainability
- Evaluate supplier controls and qualification
- Document risk-based validation strategies
- Ensure vendors meet ALCOA+, QS, and cybersecurity expectations
This is especially important given cases like Exer Labs, where vendor and supplier controls were a critical failure point.
The Bottom Line: AI Innovation Is Welcome—but Compliance Is Mandatory
AI promises enormous value for patient engagement, clinical data management, digital health, manufacturing automation, and scientific discovery. But with the FDA’s increasing scrutiny, the message is clear:
If you use AI in GxP environments, you must validate it, govern it, and control it—just like any other GxP regulated computer system across your enterprise.
USDM is here to help you do exactly that.
If your organization is experimenting with AI or already scaling AI-driven products and processes, now is the time to act.
- Start with a USDM AI Assessment to uncover risk and align with FDA expectations.
- Follow with Third-Party AI Risk Assessments to ensure your partners and tools are compliant.
- Operationalize governance with USDM’s AI Assurance Framework to confidently innovate.
Contact USDM to begin your journey to AI Assurance.
Want more AI related content for life sciences? Read our white paper on Anticipating Regulatory Compliance for Artificial Intelligence in Life Sciences or check out these AI case studies to learn more.
