Raising the Bar in GxP Compliance – Part 21: EMA vs USFDA on AI in Pharma – Navigating Divergent Regulatory Paths
Welcome to the 21st instalment of Raising the Bar in GxP Compliance, Rephine’s expert-led blog series for QA and regulatory professionals. In this edition, we dissect the emerging global approaches to regulating artificial intelligence in the life sciences sector—focusing on the EMA’s draft Annex 22 and the USFDA’s evolving AI strategy. As both agencies shape the future of AI validation and compliance in GxP environments, understanding their differences is critical. Discover how Rephine empowers organisations to align AI governance with regional expectations while maintaining global consistency and patient safety.
As AI technologies reshape pharmaceutical operations, regulators are setting distinct—but equally high—bars for compliance.
EMA and USFDA are taking divergent paths, but both demand transparency, control, and robust governance. Staying ahead means mastering both frameworks.
Understanding EMA and USFDA Approaches to AI in GxP
The regulation of artificial intelligence (AI) in the life sciences sector is evolving rapidly. Both the European Medicines Agency (EMA) and the US Food and Drug Administration (USFDA) have taken decisive steps to shape expectations around AI in GxP-regulated environments, but their approaches reflect different traditions and scopes.
This article compares the proposed EMA Annex 22—currently in public consultation—with the latest USFDA guidance and strategy documents on AI and machine learning (ML), highlighting key similarities, differences, and implications for global companies.
EMA Annex 22: AI Expectations in GMP Manufacturing
In Europe, the EMA has drafted Annex 22 to the EU GMPs, a dedicated document focused on the application of AI in manufacturing and quality systems. It outlines how GMP principles apply to systems that integrate AI or ML, especially in automated decision-making processes and data-driven controls.
USFDA AI Strategy: Broad and Device-Focused
Meanwhile, the USFDA has adopted a broader AI strategy, addressing AI/ML use in medical devices, drug development, pharmacovigilance, and clinical trials, with a series of published documents and an AI Action Plan.
Shared Regulatory Priorities: Transparency, Data Integrity, and Risk
Both EMA and USFDA agree on several core principles for the responsible use of AI:
- Transparency and traceability: Both frameworks emphasize the need for explainable AI and traceable input-output relationships.
- Data governance: Proper data quality, control of training datasets, and preservation of ALCOA+ principles are essential in both.
- Risk-based approach: Whether validating a fixed model or handling adaptive AI systems, risk assessments guide regulatory expectations.
EMA vs USFDA: Key Differences in AI Regulation
📌 Regulatory Scope
- Annex 22 is strictly tied to GMP environments, targeting AI used in manufacturing, QC, and QMS.
- USFDA guidance spans beyond GMP, covering real-world data, AI in clinical settings, and device software functions (SaMD).
📌 Adaptive Learning Models
- EMA prefers fixed models or tightly controlled systems, with change management integrated into QMS.
- USFDA supports the use of “Predetermined Change Control Plans (PCCP)”, allowing certain learning algorithms to evolve under predefined rules.
📌 Implementation Maturity
- Annex 22 is still under consultation and not yet enforceable, but signals future mandatory compliance in the EU.
- USFDA already applies its recommendations in the premarket review of AI-based products, especially for medical devices.
How Pharma Companies Can Align with Both Frameworks
To operate globally, companies should:
- Harmonize their validation strategies across regions.
- Develop robust AI governance frameworks that meet both EMA and USFDA expectations.
- Integrate AI-specific risk management and change control into their PQS.
- Monitor developments closely—Annex 22 may evolve before final publication, and the USFDA is expected to issue further guidance soon.
Final Thoughts: Aligning AI Innovation with Global Compliance
The rise of AI in GxP environments demands both innovation and compliance. While the EMA focuses on manufacturing integrity, and the USFDA on regulatory flexibility, both agree that trust, transparency, and technical robustness are non-negotiable. Forward-looking pharma and biotech firms must align with both to maintain global regulatory acceptance and patient safety.
Dr. Eduard Cayón
CSO (Chief Scientific Officer)
About the Author:
Dr. Eduard Cayón is the Chief Scientific Officer (CSO) at Rephine, a global leader in GxP compliance and quality assurance.
We don’t just deliver audits or consultancy services — we partner with clients at every stage of their quality journey, offering end-to-end solutions that empower confidence and compliance.
With over 25 years of experience, Rephine has built an enviable reputation as the gold standard in the industry operating from four primary locations: Stevenage in the UK, Barcelona in Spain, India, and Shanghai in China.
Dr. Cayón, who holds a Ph.D. in Organic Chemistry, is a deeply experienced pharmaceutical industry consultant and auditor.
He is dedicated to supporting pharmaceutical, biotech, and medical device companies in meeting the highest standards of manufacturing and supply chain integrity.
