AI Essentials for Clinical Trials (GCP-Compliant)

AI is already entering clinical trial work—sometimes formally, often informally. This course addresses that reality by helping teams understand how AI can be used responsibly within a GCP-regulated environment.

AI Essentials for Clinical Trials (GCP-Compliant) focuses on practical decision-making rather than tools. It introduces what AI is (and is not), where it can support clinical activities, and where its use raises risks from a data integrity, oversight, or compliance perspective. A risk-based approach is applied throughout, reinforcing the need for human oversight, clear boundaries, and documented rationale in line with ICH E6(R3) and E8(R1).

As an entry point, the course establishes a shared baseline across functions—supporting consistent understanding before AI is adopted or scaled.

Delivered as a self-paced eCourse (~60–70 minutes), it provides a structured and accessible way for both 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 way, without creating compliance risk.

It supports teams at different stages of adoption. Before introducing AI tools, it helps align on what is appropriate use and where clear boundaries are required, including data protection and tool selection. Where AI is already being used informally, it brings structure by addressing risks such as use of public tools, data exposure, and unverified outputs, and by clarifying roles and responsibilities.

It is also relevant when defining internal policies or SOPs, providing a foundation for decisions on approved tools, intended use, validation expectations, and documentation. As part of onboarding or awareness programs, it ensures that new team members understand how AI use differs in a GCP environment.

Finally, it supports audit and inspection readiness by reinforcing expectations around traceability, documentation, and human accountability.

The included risk scenarios, such as data exposure, hallucination, and incomplete context, anchor these concepts in realistic situations and decision-making.