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About this course

Navigating the course

Before we begin...

Profile & Expectations

1. Introduction to AI concepts and their relevance in GxP-regulated industries

4. AI Lifecycle

5. ML & LLM Lifecycle: Key Differences from Conventional CSV

6. Assignment Case Study: AI application in lifesciences - Challenges for Auditors

7. Assignment: Test

2. List of Regulations, Directives, Guidelines and Industry Standards

3. Auditing to FDA Guidance on AI/ML Software as a Device (SaMD)

Lesson 4: Auditing to EU and MHRA AI regulations

Assignment: Test

1. Introduction to Statistical Concepts Relevant to AI/ML

2. Performance Metrics for Different Types of ML Models

3. Cross-validation techniques and their importance in ML

4. Cross-validation in LLMs

5. Examples of real-world and potential Impact with Cross-Validation Flaws

6. Understanding Overfitting, Underfitting and the Bias Variance Tradeoff

7. Benchmarking practices in AI

8. Statistical significance and power analysis in AI studies

9. Assignment: Test

1. AI Risk Frameworks

2. Risk Identification and Categorization

3. Importance of Monitoring in Adaptive Models

Quiz

1. Bias, Ethics, and Fairness in GxP AI Systems

Quiz

Data Governance and Security for AI - Checklist

3. Continuous Monitoring and Validation of AI Systems

4. Assignment: Case Study - Auditing a Continuous Learning AI System for Adverse Event (AE) Detection from wearables