Description

As artificial intelligence becomes integrated into GMP operations, organizations must ensure that AI does not replace appropriate human judgment, accountability, or quality oversight. A Human-in-the-Loop (HITL) approach establishes defined points where qualified personnel review, challenge, approve, or override AI-generated recommendations.

This webinar explains how HITL can be incorporated into GMP systems to support data integrity, risk management, validation, and inspection readiness.

Learning Objectives:-

Participants will learn how to:

• Define Human-in-the-Loop in a GMP environment.
• Determine when human review of AI output is necessary.
• Establish clear authority to accept, reject, or override AI recommendations.
• Document human decisions and AI-assisted actions.
• Apply HITL controls using a risk-based and lifecycle approach.
• Maintain data integrity and traceability when AI supports GMP decisions.
• Generate inspection-ready evidence demonstrating effective human oversight.

Key Topics:-

1. What Human-in-the-Loop Means in GMP

• AI supports rather than automatically replaces critical human decisions.
• Qualified personnel retain responsibility for GMP decisions.
• Human intervention points should be predefined.

2. Risk-Based Human Oversight

• Higher-risk AI decisions require stronger human controls.
• Define which outputs require review or approval.
• Establish escalation criteria for uncertain or abnormal AI outputs.

3. Human Review and Override

• Personnel must be able to challenge AI recommendations.
• Override authority and responsibilities should be clearly defined.
• Overrides should be documented and traceable.

4. Data Integrity

• Maintain traceability between AI output and the final human decision.
• Preserve relevant inputs, outputs, approvals, changes, and overrides.
• Ensure audit trails support reconstruction of significant decisions.

5. Lifecycle Monitoring

• Monitor AI performance after implementation.
• Periodically assess whether human oversight remains effective.
• Review deviations, overrides, unexpected outputs, and performance trends.
• Reassess controls when the AI system, data, intended use, or risk changes.

6. Inspection-Ready Evidence

Organizations should be able to demonstrate:

• Who reviewed the AI output.
• What information was reviewed.
• When the review occurred.
• What decision was made.
• Why an AI recommendation was accepted or overridden when justification is required.
• How AI performance and human oversight are monitored over time.

Who Should Attend:-

• Quality Assurance and Quality Control
• Validation and Computer System Validation personnel
• Manufacturing and Operations
• Data Integrity professionals
• Regulatory Affairs
• IT and Digital Transformation teams
• AI/ML system owners
• Compliance and auditing personnel.