Potential of Artificial Intelligence in Improving Speed and Efficiency of Clinical Trials

Abstract

Abstract In recent years, artificial intelligence (AI) has demonstrated its ability to improve many facets of health care. Clinical trials are no exception. There s enormous potential in the use of AI in all stages of clinical trials – planning, conduct, and analysis. However, clinical trials are highly regulated, and the guidance on the use of AI in trials has not yet been developed. Once these challenges are overcome or in the appropriate setting, AI can offer significant speed and cost advantage across all stages of clinical trials. In the planning stage, AI can simulate patient journey to enable patient-centric study design and identify the patient pool and investigational sites based on eligibility criteria. The tools can run multiple scenarios on timeline, cost, and complexity to determine feasibility. Generative AI can be used to create study documents. During study conduct, AI can be deployed to identify risks for data quality and integrity enabling reduction in monitoring visits and potentially reducing carbon footprint. AI can predict event accrual and data cleaning using machine learning. During study closeout, AI can accelerate data query resolution by bots and reduce site burden. The generative AI applied during the creation of study documents can be efficiently used to write study reports and generate tables and figures. In summary, AI has the potential to improve the efficiency of clinical trials, and if development is successful, AI can even potentially reduce the cost of drug to the patient.

Ähnliche Arbeiten