AI-Enabled Risk-Based Monitoring for Spirometry

This document is a science brief that discusses AI-enabled risk-based monitoring (RBM) specifically for spirometry in clinical trials. Spirometry is a vital pulmonary function test used to evaluate lung function by measuring airflow and lung volumes. The brief outlines the importance of RBM in ensuring data integrity and quality during clinical trials, particularly in multicenter studies where variability can affect outcomes. It details how AI can enhance the monitoring process by identifying data anomalies and potential inconsistencies among participants, thereby optimizing the reliability of spirometry results. The document also explains how AI algorithms can create personalized spirometry profiles and facilitate longitudinal comparisons of data from the same participant over time. This approach aims to detect implausible deviations and improve the overall quality of clinical investigations, ensuring that the data submitted for regulatory review is accurate and trustworthy. The brief concludes with a discussion of the benefits of integrating AI into RBM practices for respiratory trials.