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AI in Life Sciences > Intermediate
AI Governance for Pharma & Life Sciences
Understand how to govern AI responsibly in regulated pharmaceutical and life-science environments. This course covers AI oversight frameworks, validation requirements, risk classification, and compliance with EU AI Act, FDA AI guidance, and ICH guidelines. Designed for quality, regulatory, and technical teams implementing AI in GxP environments.
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What you'll learn
- Understand how to govern AI responsibly in regulated pharmaceutical and life-science environments
Course content
Welcome to the Course
What You Will Learn in AI Governance for Pharma
What AI Governance Means in Life Sciences
Why Pharma Cannot Use AI Like Other Industries
The Cost of Poor AI Oversight in Regulated Work
Key Governance Roles Across Pharma Functions
AI Machine Learning LLMs and Agentic Systems Explained Simply
How AI Systems Behave Drift and Fail
What Makes Generative AI Different from Traditional Automation
Why Human Oversight Still Matters
Global Regulatory Direction on AI in Life Sciences
FDA EMA and International Expectations
GxP Thinking Applied to AI Systems
What Inspectors and Auditors Will Care About
Building a Risk-Based Approach to AI Use
Low-Risk vs High-Risk AI Use Cases in Pharma
Risk Identification Impact and Mitigation Planning
Escalation and Decision Rights for AI Risk
Human-in-the-Loop Human-on-the-Loop and Human-in-Command
WHO Owns the Output When AI Is Used
Oversight Models for Medical Safety Regulatory and Quality Teams
Preventing Overreliance and Automation Bias
What Validity Means for AI in Regulated Environments
Testing Performance Reproducibility and Reliability
Monitoring Change Drift and Model Degradation
Validation Thinking for AI-Enabled Workflows
What Must Be Transparent About an AI Solution
Explainability for Users Reviewers and Auditors
Documentation Traceability and Version Control
Making AI Outputs Reviewable and Defensible
Sensitive Data and Privacy Risk in Pharma AI
Using AI with Patient Safety and Study Data
Confidentiality Access Control and Vendor Risk
Ethical Use Bias Fairness and Equity
What a Practical AI Governance Framework Looks Like
Policies Standards Review Boards and Approval Paths
Cross-Functional Governance Between Business Quality and It
Governance for Internal Tools vs Vendor Tools
AI Governance for Pharmacovigilance Use Cases
AI Governance for Regulatory Affairs and Medical Writing
AI Governance for Clinical Operations and Data Workflows
AI Governance for Quality Compliance and Knowledge Work
How to Launch an AI Governance Program Step by Step
Common Governance Mistakes and How to Avoid Them
AI Governance Skills for Pharma Professionals
Your 30-Day AI Governance Action Plan
Requirements
- No prior experience required unless stated in the lessons.
Description
Understand how to govern AI responsibly in regulated pharmaceutical and life-science environments. This course covers AI oversight frameworks, validation requirements, risk classification, and compliance with EU AI Act, FDA AI guidance, and ICH guidelines. Designed for quality, regulatory, and technical teams implementing AI in GxP environments.
This course is designed for:
- SafeMeds Academy learners
- Life-science professionals building practical confidence
Instructor
SafeMeds Academy
Life-science curriculum specialist
Practical training for pharma, biotech, CRO, regulatory, clinical, PV, market access, medical writing, data, and AI-enabled teams across the globe.
How to earn your certificate
- Watch the lessons. Each lesson is marked complete automatically once you have watched 90% of its video — there is no button to click.
- Reach 100%. When all 46 lessons are complete, the final quiz unlocks in your course player and dashboard.
- Pass the final quiz. 20 multiple-choice questions, 70% to pass (14/20), with unlimited retries.
- Get your certificate. It is issued to your account automatically, emailed to you, and verifiable online.