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AI Validation, GAMP 5 & GxP Compliance for Life Sciences Course & Certification
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AI Validation, GAMP 5 & GxP Compliance for Life Sciences
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About
AI Validation, GAMP 5 & GxP Compliance for Life Sciences training provides practical knowledge for implementing, validating, and maintaining AI-enabled computerized systems in regulated pharmaceutical and life sciences environments. The course explains how AI technologies can be applied while meeting GxP requirements, ensuring data integrity, and protecting product quality and patient safety.
This AI Validation, GAMP 5 & GxP Compliance for Life Sciences Course & Certification covers GAMP 5 risk-based validation principles, computerized system validation (CSV), AI/ML model lifecycle management, intended use, risk assessment, data integrity (ALCOA+), audit trails, documentation, change control, performance monitoring, supplier management, and regulatory expectations for AI-enabled systems. Upon successful completion, learners receive a certification demonstrating their understanding of AI validation and GxP compliance best practices.
Who Should Enrol?
- Quality Assurance (QA) and Quality Control (QC) Professionals
- Validation, CSV, and Computer System Assurance (CSA) Professionals
- Regulatory Affairs and Compliance Specialists
- Data Scientists, AI/ML Engineers, and Digital Transformation Teams
- IT, Software, and System Owners in GxP Environments
- Manufacturing, Engineering, and Automation Professionals
- Clinical Research and Laboratory Personnel
- Anyone involved in implementing, validating, or governing AI systems in life sciences
What you will learn
Understand the fundamentals of AI validation, GAMP 5 principles, GxP requirements, and their role in ensuring compliant AI systems in life sciences.
Learn risk-based validation approaches, AI/ML model lifecycle management, computerized system validation (CSV), and change control processes.
Develop knowledge of data integrity (ALCOA+), audit trails, model training and testing, documentation, and regulatory expectations for AI systems.
Gain practical understanding of AI governance, performance monitoring, model updates, supplier oversight, and maintaining validated AI systems throughout their lifecycle.
Course Syllabus
- What counts as AI, machine learning and generative AI - and why behaviour matters more than labels
- AI-enabled systems across the GxP lifecycle: manufacturing, QC, clinical, pharmacovigilance, quality and regulatory
- Four properties that raise the assurance bar: data dependence, opacity, drift and autonomy potential
- Classifying use cases from low-risk productivity tools to regulated decision-making
- The influence x consequence risk matrix and the AI use-case inventory
- Core GAMP 5 principles: risk-based assurance, critical thinking and leveraging the supplier
- Lifecycle thinking for AI: concept, project, deploy and operate phases
- Intended use and fitness for purpose as the anchor of assurance decisions
- Supplier assessment for AI providers and closing residual evidence gaps
- Scalable, living documentation: traceability over volume
- Writing a specific, bounded and testable question of interest
- Intended use versus actual use - detecting and controlling use creep
- Context of use: advisory, influential and determinative outputs and the assurance bar
- Boundaries, exclusions, limitations and out-of-range handling
- Converting intended use into validation requirements and oversight design
- Scoring model influence and decision consequence: severity, scale, reversibility and detectability
- Three impact lenses: patient safety, product quality and data integrity
- Automation bias and why human oversight can quietly fail
- False positives versus false negatives - the error asymmetry in GxP
- Residual risk acceptance, escalation thresholds and governance routing
- Why data quality bounds every performance claim - governing data before validating models
- ALCOA+ applied to training, tuning, test and production data
- Data provenance, lineage and audit trails for data preparation
- Representativeness, sampling bias, label bias and under-represented cases
- Labelling controls, privacy, access control and third-party data risk
- From intended use and risk tier to a proportionate validation strategy
- Testable functional, performance, data and operational requirements
- Sensitivity, specificity, precision and why accuracy alone deceives on imbalanced data
- Confidence intervals, per-case uncertainty and acceptance criteria set before testing
- Explainability proportionate to risk, verification versus validation, and documented deviations
- Designing genuine review points: information, time and competence to disagree
- Escalation pathways for low-confidence, borderline and novel cases
- Override governance - what override rates reveal about model fit and reviewer engagement
- Countering automation bias with evidence, vigilance checks and workload management
- Segregation of duties, exception handling and defensible decision trails
- Monitoring production performance against validation metrics
- Data drift, concept drift and performance decay - and their different responses
- Alert thresholds paired with predefined actions
- Versioning model, data and configuration together; change-control triggers
- Retraining, revalidation, regression testing, periodic review and controlled retirement
- Why you can outsource the model but never the accountability
- Assessing AI suppliers on data and model transparency, not just IT quality
- Cloud and SaaS AI: data residency, multi-tenant security and visibility of model changes
- Large language models: input confidentiality and plausible-but-wrong outputs
- Technical and quality agreements, change notification, audit rights, exit strategy and continuity
- What inspectors and auditors ask about AI-enabled systems
- The model inventory and per-system evidence packs
- The AI governance committee, roles and SOP framework embedded in the existing QMS
- AI incidents through CAPA and a maturity view of AI governance
- The 90-day roadmap from ad hoc AI use to a defensible, improving system
- 📘 Bonus: AI Validation, GAMP 5 & GxP Compliance for Life Sciences eBook (Free with purchase)
Course Benefits
eBook
Access the course content in eBook format, which can be downloaded and read on your device.
CPD Points
Gain Continuing Professional Development (CPD) Points, accredited by The Faculty of Pharmaceutical Medicine of the Royal College of Physicians of the United Kingdom. These can be used to count towards the distance learning element of any scheme that comes under the umbrella of The Academy of Medical Royal Colleges or any other scheme for which there is mutual recognition.
Certification
Receive a personal certificate to show your subject knowledge on course completion.
Affordable
You get excellent value through our cost-effective prices. We can also offer you group discounts on larger purchases.
Flexibility
The course saves you time through the convenience of online availability. This lets you complete the interactive course at your own comfort.
Keep Up to Date
You will stay up to date with evolving regulatory expectations for AI in regulated environments, including risk-based GxP assurance principles, GAMP 5 lifecycle thinking, ALCOA+ data-integrity expectations and current inspection practice for AI-enabled systems. Course updates are provided as industry guidance develops, at no extra cost during your licence period.
Learn from Industry Experts
This course was developed by GxP quality and validation specialists with practical experience of computerised system validation, data integrity and AI governance across pharmaceutical, biotechnology and medical-device operations. Every module ties regulatory principles to real regulated decisions through cross-functional case studies spanning manufacturing, QC, clinical, pharmacovigilance and quality operations.
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