Good AI Practice (GAIP) in GxP Systems

Comprehensive training curriculum bridging traditional pharmaceutical regulatory requirements with the rapidly evolving field of Artificial Intelligence and Machine Learning in healthcare.

Pharmaceutical Regulatory Compliance and AI Integration
10 Comprehensive Chapters
70+ Expert Topics
100% GxP Compliant
AI/ML Lifecycle Focus

Course Overview

Target Audience

  • Clinical Data Scientists
  • System Sponsors
  • Quality Assurance Professionals
  • Regulatory Affairs Specialists
  • Clinical Trial Investigators utilizing AI

Course Level

  • Beginner to Intermediate
  • No prior AI experience required
  • GxP knowledge recommended
  • Self-paced learning modules
  • Industry certification included

Key Frameworks

  • FDA AI/ML Regulatory Framework
  • EU AI Act Compliance
  • EMA AI Guidance
  • ICH-GCP E6 R3 Alignment
  • ALCOA+ Data Principles

Complete Curriculum

01

Introduction to GAIP

Core principles and foundations
›
  • The History of AI in Clinical Research and Pharma (Parts 1-3)
  • What is Good AI Practice (GAIP) / Good Machine Learning Practice (GMLP)?
  • The Core Principles of GAIP in a GxP Environment
  • Quality Assurance (QA) vs. Quality Control (QC) in AI Systems
  • Documentation, Code Versioning, and MLOps Control
  • Key Resources: Global AI Guidelines (FDA, EU AI Act, EMA)
02

Regulatory Authorities and AI Ethics Committees

Governance and ethical oversight
›
  • Introduction to Global AI Regulations (Parts 1-5)
  • Responsibilities of Regulatory Authorities for SaMD and AI
  • Responsibilities of the AI Ethics Committee / Algorithm Review Board
  • Patient Data Informed Consent for AI Model Training
  • Ensuring Algorithmic Fairness, Explainability, and Transparency
  • Ethics Committee Interactions with System Sponsors and Developers
03

Developer & Data Scientist Responsibilities

Technical and compliance requirements
›
  • Developer Qualifications and Technical Agreements
  • Adequate Computational Resources and Data Environments
  • Compliance with the AI Protocol / Intended Use Cases
  • Managing Algorithmic Bias and Mitigating Risks
  • Human-in-the-Loop (HITL) Medical Care Safeguards
  • Records and Reports: Source Code, Repositories, and Dataset Logs
  • Premature Termination or Suspension of an AI System
04

Sponsor & System Owner Responsibilities

Quality management and oversight
›
  • Quality Management for AI/ML Systems (Parts 1-3)
  • QA and QC: AI-Specific Standard Operating Procedures (SOPs)
  • Vendor Management: Cloud Service Providers and AI Contractors
  • AI Project Design and Lifecycle Management
  • Data Management, Encryption, and Cybersecurity
  • Algorithm Manufacturing: Training, Validation, and Testing Environments
  • Supplying and Handling AI Updates (Continuous Learning vs. Locked Models)
  • Audit and Inspection Readiness for AI Models
  • Clinical Trial/Study Reports Involving AI Endpoints
05

Data Governance

ALCOA+ principles and validation
›
  • Introduction to AI Data Governance and ALCOA+ Principles
  • Maintaining the Blind (Preventing Data Leakage in Training/Testing Sets)
  • The AI Data Life Cycle: Collection, Cleaning, Labeling, and Storage (Parts 1-4)
  • Computerised Systems Validation (CSV) / Computer Software Assurance (CSA) for AI (Parts 1-7)
  • Data Privacy, Anonymization, and Pseudonymization Techniques
06

AI Auditing and Continuous Monitoring

Post-deployment surveillance
›
  • Introduction to AI Post-Deployment Monitoring
  • Monitoring for Model Drift, Data Drift, and Concept Drift
  • The Monitoring Visit / System Audit Review
  • Verifying Regulatory and Protocol Compliance
  • Verifying Output Data Integrity
  • Quality Management - Centralised AI Monitoring
  • Fraud, Misconduct, and Data Manipulation (Parts 1 & 2)
  • The Monitoring Report and Corrective Action Plans
07

Safety & Algorithmic Error Reporting

Risk management and adverse events
›
  • Introduction to Algorithmic Safety
  • Defining AI Errors, Hallucinations, and Catastrophic Failures
  • Serious Adverse Events (SAEs) Linked to AI Clinical Decisions
  • Reporting Timelines for Unanticipated Software Problems
  • Periodic AI Safety and Performance Updates
08

AI System Protocol and Amendments

Change control and specifications
›
  • Introduction to AI Model Architecture Specifications
  • Protocol Structure and Content: Defining Boundaries and Limitations (Parts 1-3)
  • Managing Change Control for Model Retraining and Amendments
09

Model Documentation

AI model cards and operator manuals
›
  • Introduction to the AI Model Card
  • Documenting Hyperparameters, Inputs, Outputs, and Known Limitations
  • Instructions for Clinical Use and Operator Manuals
10

Essential Documents and Audit Trails

Archiving and compliance documentation
›
  • Archiving Code, Model Weights, and Historical Datasets
  • Pre-Deployment Essential Documents
  • Post-Deployment Essential Documents
  • Glossary & Abbreviations (Mapping AI to GxP terminology)

Course Features

Regulatory Compliance Icon

Industry Certification

Uniquely numbered, personal certificate demonstrating GAIP subject knowledge, fully compliant with industry training records and audit requirements.

Data Governance Visualization

Comprehensive Resources

Access to global competent authorities lists, mapping charts for GCP-to-GAIP equivalents, validation checklists, and downloadable handouts.

Monitoring Dashboard

Practical Applications

Real-world case studies, monitoring frameworks, drift detection strategies, and hands-on examples from pharmaceutical AI deployments.

Ready to Master Good AI Practice?

Join the next generation of AI-enabled healthcare professionals with comprehensive GAIP certification