Innovation with Intention: Safeguarding Data Integrity in the Age of AI

• Recognize and articulate the key risks associated with the use of Artificial Intelligence in Clinical Data Management
• Apply practical, governance-forward strategies
• Reframe AI as a governed collaborator rather than a replacement for human judgment

ACDM members and non-members who would like to join an open discussion concerning a Hot Topic in Data Management.

Artificial Intelligence is rapidly embedding itself into Clinical Data Management (CDM), transforming how data is reviewed, queried, monitored, and interpreted. From anomaly detection to predictive risk signaling, AI promises speed and scalability—but with that promise comes a quieter, more complex challenge: how do we govern intelligence we no longer fully see or understand?

This session argues that the true risk of AI in CDM is not technological failure, but misplaced trust. As AI-driven tools influence data decisions traditionally grounded in human judgment, subtle vulnerabilities emerge—algorithmic bias, over-automation, and the gradual erosion of data stewardship. These risks often remain invisible until they manifest as data integrity issues, regulatory scrutiny, or compromised study conclusions.

Taking a ”devil’s advocate” stance, this presentation examines where AI use in CDM can unintentionally amplify error or create false confidence. Drawing on emerging industry practices, regulatory expectations, and real-world operational insights, the session explores how traditional validation and Risk-Based Data Monitoring (RBDM) approaches fall short when applied to adaptive, learning systems.

Rather than resisting innovation, this talk introduces a governance-forward framework for responsible AI adoption in CDM. Topics include human-in-the-loop oversight, explainability requirements, accountability mapping, continuous model performance review, and the evolution toward AI-aware data governance models.

Ultimately, this session reframes the conversation: AI should not replace clinical data judgment—but it can strengthen it, if governed with intention. Because in clinical research, the devil is rarely in the algorithm—it’s in the data decisions we stop questioning.

MEET THE SPEAKER:

Minya Engelbrecht is a Senior Clinical Data Team Lead with extensive experience in clinical data management within clinical research. With a background in Academics and Psychology, she brings a strategic, people-informed approach to data quality and integrity. She is passionate about precision, regulatory compliance, and translating complex clinical data into meaningful insights that support high-quality trial outcomes.