As artificial intelligence increasingly transforms the life sciences landscape, regulatory clarity is more and more critical—particularly in GxP-regulated environments. The incorporation of AI into drug development, production, and quality systems requires not just innovation but careful control to guarantee patient safety and data integrity. As global regulators move to set limits and expectations, comprehension of the subtleties of new AI frameworks is no longer a nicety—it's mandatory for compliance professionals and technology planners both.

This blog represents a comprehensive comparative analysis of the three most important AI regulation realized so far —EU AI Act (finalized), EU GMP Annex 22 (draft), and FDA AI Draft Guidance - Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (draft) —with a key focus on their significance to GxP-regulated environments and implementation-viewpoint insights.

	Implementation Area	EU AI Act	EU GMP Annex 22	FDA AI Draft Guidance
1	Intended Use Definition	Acknowledge documented intended use and register in EU database for high-risk AI systems.	Detail intended use, including characteristics and limitations of input data. SME approval prior to testing	Define the question of interest and context of use (COU). Clarify role of model and scope in decision-making.
2	Risk Classification & Model Type	Categorize AI systems as high-risk on the grounds of impact on health, safety, and basic rights. Emotion recognition in work environment and predictive profiling are the prohibited practices.	Static deterministic models permitted in mission-critical GMP applications only. Dynamic/adaptive models and probabilistic outputs are not permitted.	Measure model risk by model influence × decision consequence. Utilize risk matrix for establishment of credibility assessment rigor.
3	Testing & Validation	High-risk AI products need conformity assessment (third-party or internal). CE marking and post-market surveillance are required.	Establish test measures (e.g., F1 score, accuracy, sensitivity, specificity). Acceptance levels need to be at least as good as replaced process. Record test plan, deviations, and keep all records.	Provide performance measures with confidence limits. Specify evaluation techniques, test data independence, and consistency with observed data.
4	Data Governance	Training/validation/testing data should be high quality, representative, and bias-reduced. Special categories of personal data can be processed under strong protections.	Use representative, stratified, validated test data. Generative data should be avoided unless warranted. Data independence and audit trails must be ensured.	Data has to be fit-for-use: applicable, trustworthy, traceable. Document data management practices and bias reduction.
5	Explainability & Transparency	User instructions need to contain system capabilities, limitations, risks, and control mechanisms. Transparency needed in synthetic content and biometric systems.	Feature attribution with SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), or heatmaps. Explain why features are relevant.	Write down model architecture, features, training reasons. Add uncertainty quantification and explainability tools.
6	Human Oversight	Mandatory human oversight for high-risk AI. Biometric systems require dual human verification unless legally exempt.	HITL required for non-critical applications. Operator training and performance monitoring mandatory.	Human-AI team performance must be evaluated. Oversight roles and responsibilities must be defined.
7	Lifecycle Management & Change Control	Change control and post-market surveillance necessary. Significant changes initiate reassessment.	Adopt change and configuration control. Keep track of performance drift and sample space of input. Retest if there are changes.	Lifecycle maintenance necessary. Retrain/revalidate in case of change in performance. Document changes according to regulatory guidelines.
8	Regulatory Engagement	Interact with AI Office and federal authorities. Apply regulatory sandboxes for testing and innovation.	No formal engagement processes established; follows Annex 11 principles.	Early interaction highly recommended. Apply Pre-IND, CID, ISTAND, and other FDA initiatives.
9	Documentation & Traceability	Keep technical documentation, EU declaration of conformity, and register systems on EU database.	Keep all test documentation, access control logs, and audit trails.	Prepare credibility assessment report. Cover model development, evaluation, and deviations.
10	Confidence & Thresholds	Specify thresholds for high-risk AI forecasts. Employ confidence scores to ascertain reliability.	Store confidence scores. Utilize thresholds to mark undecided results.	Estimate uncertainty and confidence levels. Incorporate into performance measures.
11	Personnel Qualification & Training	Providers and deployers must ensure that personnel who take part in the operation of AI systems are AI literate and trained.	All personnel working on AI lifecycle should have their roles defined and appropriate qualifications.	Personnel working on model development and monitoring should be trained and qualified.
12	SME & Innovation Support	SMEs and start-ups receive priority access to sandboxes, simplified QMS, reduced fees, and standard templates.	Not specifically mentioned.	Not specifically mentioned.
13	Real-World Testing	Real-world testing allowed under close control; requires testing plan, consent, supervision, and registration.	Not applicable; testing must be controlled and documented.	Real-world monitoring of performance encouraged as part of lifecycle maintenance.
14	Biometric & Emotion Recognition Systems	Emotion recognition in educational/work environment not allowed; judicial approval and double human endorsement required for biometric systems.	Not applicable; emotion recognition and biometric categorization excluded.	If used, justification and tested for bias and risk required.
15	Fundamental Rights Impact Assessment	Compulsory for public deployers and private deployers of high-risk AI in high-concern domains (e.g., healthcare, education, law enforcement). Optional.	Optional.	Risk-based credibility testing involves ethics.
16	Adversarial Testing & Robustness	Suppliers of general-purpose AI models with systemic risk are obliged to perform adversarial testing (internal or external) before market launch.	Not clearly mentioned but robustness is implied through validation and performance tracking.	Sponsors ought to test model robustness and resilience to input variation and overfitting.
17	Cybersecurity & Model Protection	Cybersecurity controls required for high-risk and systemic-risk models, including model leakage protection, access control, and tampering.	Configuration control and detection of unauthorized change required.	Sponsors must ensure model integrity and security throughout lifecycle, especially in production.
18	Environmental Sustainability & Ethical Design	Voluntary codes of conduct promoted for energy-efficient AI, inclusive design, and ethical development.	Not mentioned.	Ethics are part of credibility assessment but not specifically linked to sustainability.
19	Stakeholder Participation & Inclusive Development	Encourages participation of civil society, academia, and various development teams in AI system design.	Requires SME, QA, IT, and data scientist collaboration.	Sponsors need to have subject matter experts included for model development and risk evaluation.
20	Voluntary Codes of Conduct for Non-High-Risk AI	Non-high-risk AI system providers and deployers should voluntarily adopt high-risk requirements (e.g., transparency, oversight, documentation).	Principles may be applied to non-critical GMP uses with HITL.	No official voluntary scheme, but sponsors may apply credibility principles to non-regulatory AI application.
21	Transition Periods & Legacy Systems	Market-placed AI systems before Aug 2026 must be compliant if significantly modified. Public sector systems must comply by Dec 2030	New systems only; legacy systems may potentially need revalidation if AI is introduced.	No transition period is provided, but changes during the lifecycle must be documented and justified.
22	Serious Incident Reporting	Providers shall report serious events (e.g., harm to health, disruption of infrastructure, infringement of rights) to the competent authorities.	Deviations and failures shall be logged and investigated.	Sponsors shall track and report side effects associated with the application of AI models.

Final Insights for GxP Professionals

EU GMP Annex 22: Provides straightforward validation guidelines for static AI models deployed in GMP production environments. It emphasizes documentation, explainability, and human oversight to ensure product quality and patient safety.

FDA Draft Guidance: Provides a framework for credibility to make AI models credible in regulatory submissions. It applies a risk-based methodology with context of use and early collaboration with the FDA.

EU AI Act: Requires lawfulness for high-risk and general-purpose AI, with emphasis on safety, transparency, and regulation. It brings strict obligations to providers, such as risk management and technical documentation.

Conclusion:

Yet the regulatory landscape for AI is far from settled. While the EU AI Act is finalized, the EU GMP Annex 22 and the FDA AI Draft Guidance are still in draft form. Both are open to revisions on the basis of industry input, advancements in technology, and shifting risk views. Organizations therefore need to remain adaptable and up-to-date—because draft today may become policy tomorrow.

Stay tuned for Part 2 of this blog, where we’ll explore updates as these drafts evolve, and dive deeper into practical implementation strategies for GxP environments.

Reference:

1. The EU AI Act - https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689

2. EU GMP Annex 22: Artificial Intelligence - https://health.ec.europa.eu/document/download/5f38a92d-bb8e-4264-8898-ea076e926db6_en?filename=mp_vol4_chap4_annex22_consultation_guideline_en.pdf

3. FDA - Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products - https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological