Good AI Practice (GxP-AI)
From Regulatory Guidance to Operational Readiness
In January, FDA and EMA introduced the Good AI Practice framework to guide the development, validation and monitoring of AI systems in regulated environments.
Their 10 principles are tailored to the drug development cycle and emphasize the importance of:
- 1. Human-centric by design
- 2. Risk-based approach
- 3. Adherence to standards
- 4. Clear context of use
- 5. Multidisciplinary expertise
- 6. Data governance and documentation
- 7. Model design and development practices
- 8. Risk-based performance assessment
- 9. Life cycle management
- 10. Clear, essential information
What is Good AI Practice?
The Good AI Practice framework defines regulatory expectations for AI systems operating in GxP environments, with a focus on governance, data integrity, lifecycle validation and continuous monitoring.
• Model governance and accountability
• Data integrity and traceability
• Lifecycle validation and continuous monitoring
• Model governance and accountability
• Data integrity and traceability
• Lifecycle validation and continuous monitoring
How InSilicoTrials helps
From guidance to operational readiness - already with pharma teams.
• Translate guidance into operational AI governance fraemworks
• Train cross-functional teams (Regulatory, QA, Digital, R&D)
• Align AI systems with inspection-readiness standart
• Translate guidance into operational AI governance fraemworks
• Train cross-functional teams (Regulatory, QA, Digital, R&D)
• Align AI systems with inspection-readiness standart
AI Compliance Readiness Program
Designed for executives and non-technical roles. No AI background required.
Objectives
- • Understand regulatory intent
- • Identify compliance risks
- • Set up governance processes
Objectives
- • Interpret validation documents
- • Ask the right questions
- • Support audit preparedness
Receive more information
Get the program outline and executive briefing materials.