PS24: From Signals to Action: AI-Enabled Anomaly Detection and Central Statistical Monitoring for Transdisciplinary Oversight in Neuropsychiatric Clinical Trials

In the evolving digital era, the conduct of neuroscience and neuropsychiatric clinical trials is increasingly shaped by high-volume, near-real-time data streams, complex multi-vendor ecosystems, and digitally enabled assessments. These trials present unique challenges due to heterogeneous data sources, subjective clinical endpoints, and substantial site- and rater-level variability, placing new demands on timely, coordinated, and defensible transdisciplinary decision-making across statistical, clinical, operational, quality, and regulatory stakeholders. Central Statistical Monitoring (CSM) has emerged as a critical enabler of this transdisciplinary decision-making in neuropsychiatric trail conduct, particularly as artificial intelligence and machine learning methods are increasingly applied to centralized oversight. By integrating diverse digital data sources and leveraging AI-enabled anomaly detection techniques, such as multivariate risk indicators, longitudinal and site-level profiling, distance-based similarity metrics, and targeted data visualizations, CSM supports proactive identification of emerging risks, prioritization of follow-up actions, and transparent documentation of sponsor oversight decisions within a risk-based quality management (RBQM) paradigm. This session will explore how AI-enabled CSM is being used in practice to transform complex data stream into actionable insights during the conduct of neuroscience and neuropsychiatric clinical trials. Speakers will discuss how statistical and machine learning derived signals are evaluated in clinical context, and implementation considerations, including governance models, cross-functional escalation pathways, and strategies for translating statistical signals into operational and quality actions. Regulatory perspectives on expectations for centralized monitoring, documentation, and inspection readiness will also be highlighted. Through practical examples and discussion, this session aims to demonstrate how central statistical monitoring can serve as a unifying and transdisciplinary tool that proactively identify and mitigate risks to data quality by detecting unusual patterns, trends, or anomalies across sites, subjects, and data domains.