2 Key Areas To Leverage AI/ML For More Successful Clinical Trials

The adoption of artificial intelligence (AI) and machine learning (ML) has been one of the fastest growing trends across industries over the past decade. With the continuous advancements in technology, access to ever more powerful computers, increased availability of clinical and research data, and rapid development of novel algorithms that analyze and utilize that data, interest in applying AI and ML to trial design and clinical trials to improve high failure rates is increasing. Among its many potential practical applications, AI and ML can be used to minimize errors in clinical trial participant management (e.g., cohort selection, patient identification and recruiting, participant retention) and streamline data management (e.g., automate data collection, monitor data quality, analyze large data sets).1 However, realizing the potential of this technology will require overcoming a range of different issues, including problems with data quality and access, transparency of underlying development and validation processes, potential bias inherent in the source data as well as the algorithm's implementation, and the lack of definitive regulatory guidance from the relevant government agencies. Selecting and recruiting patients for clinical trials is complicated and, despite the extensive time and effort companies put into clinical trial participant management, one of the biggest factors that causes a clinical trial to fail is failure to select and recruit the most suitable subjects for a trial.2