AI Essentials for Clinical Trials (GCP-Compliant)

AI is already entering clinical trial workflows, sometimes formally, often informally. Regulatory expectations are also evolving. Current GCP, computerized systems, data integrity, and AI governance guidance increasingly reinforce the need for controlled, documented, and reviewable AI use within regulated environments.

AI Essentials for Clinical Trials (GCP-Compliant) helps teams understand how AI can be applied responsibly within clinical research. The course focuses on practical decision-making rather than tools. It explains where AI may support clinical activities, where risks emerge, and why human oversight, documented rationale, and controlled use remain essential under ICH E6(R3), E8(R1), computerized systems guidance, and emerging AI governance expectations.

As an entry point, the course establishes a shared baseline across functions, supporting consistent understanding before AI is introduced, scaled, or operationalized within study environments.

Delivered as a self-paced eCourse (~60–70 minutes), it provides a structured and accessible way for individuals and teams to build confidence in applying AI within GCP expectations.

Typical use cases in practice

This module is used when organizations need to introduce AI into clinical trial activities in a controlled, GCP-aligned manner, without creating unnecessary compliance risk.

It supports teams at different stages of adoption. Before introducing AI tools, it helps align on what constitutes appropriate use and where clear boundaries are required, including data protection, approved tools, and acceptable use scenarios. Where AI is already being used informally, the course introduces structure by addressing risks such as public AI tools, data exposure, unverified outputs, and undocumented decision-making.

It is also relevant when defining internal policies, SOPs, or governance expectations. The course provides a foundation for decisions around approved tools, intended use, validation expectations, oversight responsibilities, and documentation practices. As part of onboarding or awareness initiatives, it helps establish a consistent understanding of how AI use differs within a regulated GCP environment.

The course also supports audit and inspection readiness by reinforcing expectations around traceability, reviewability, documented rationale, and human accountability.

The included risk scenarios, including data exposure, hallucination, incomplete context, and overreliance on AI outputs, anchor these concepts in realistic situations and operational decision-making.