Learn how to apply AI within clinical trials in a controlled, GCP-aligned manner, supporting computerized systems, data integrity, and human oversight expectations.
This module serves as the entry point to AI literacy and controlled AI use within a GCP-regulated environment.
It establishes a shared baseline across teams on where AI may support clinical trial activities, where risks emerge, and what must remain controlled, documented, and reviewable from a regulatory perspective.
The course complements the RBQM pathway by addressing a growing operational reality: AI is increasingly used across clinical workflows, including computerized systems and data-related activities governed under GCP expectations.
It prepares teams to apply AI within clinical trials in a controlled, inspection-ready manner before adoption or scale introduces unnecessary compliance risk.
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This module is increasingly used as a foundational AI onboarding and awareness training requirement for clinical trial teams operating in GCP-regulated environments where AI-supported and computerized systems are being introduced into daily workflows.
As regulatory expectations continue to evolve under ICH E6(R3), organizations are expected to demonstrate that personnel understand where AI may support clinical trial activities, where risks emerge, and what must remain controlled, reviewable, documented, and subject to human oversight.
The course supports organizations introducing AI into clinical trial activities in a controlled and inspection-ready manner. It helps teams understand expectations around traceability, accountability, data protection, validation considerations, documentation practices, and appropriate human review when using AI-supported technologies and computerized systems.
It is particularly relevant before scaling AI-supported workflows across functions such as Clinical Operations, Data Management, Medical Monitoring, Centralized Monitoring, Quality Assurance, and study oversight activities.
The module is also used when defining internal AI governance approaches, onboarding new employees, preparing teams for AI adoption initiatives, or establishing baseline awareness before introducing organization-specific SOPs, approved tools, and operational guardrails.
Practical risk scenarios address topics such as data exposure, hallucination, incomplete context, unverifiable outputs, inappropriate use of public AI tools, and overreliance on automation, helping teams apply these concepts in realistic operational situations and decision-making.
The training reinforces that AI-supported activities within clinical trials remain subject to GCP expectations, sponsor oversight responsibilities, and human accountability, even when automation or AI-assisted technologies are used.
