The Power Of AI To Improve Clinical Trial Monitoring

By Nicola Phillips and Tameka Johnson, inSeption Group

Artificial intelligence already serves several applications in clinical settings, but building trust in AI systems, ensuring the accuracy of the data they generate, and determining their return on investment (ROI) all remain a challenge. Narrowing AI’s clinical monitoring use to fit-for-purpose tasks, increasing transparency in decision-making, and establishing clear ROI metrics — both during the trial and after it concludes — are key steps toward improving its effectiveness in clinical monitoring.

AI in clinical trials must evolve alongside standards. Algorithm, personnel training, and post-trial AI performance reviews must be regular and consistent to ensure data security, patient safety, and smooth usability. As with most clinical trial-related elements, both the training and system updates must be clearly documented.