Applications of artificial intelligence in drug development using real-world data

TL;DR

This paper reviews how artificial intelligence techniques are increasingly used with real-world data to improve various stages of drug development, highlighting recent trends, applications, and future research directions.

Contribution

It provides a comprehensive overview of the integration of AI and RWD in drug development over the past 20 years, identifying key applications and research gaps.

Findings

AI is mainly used for adverse event detection, trial recruitment, and drug repurposing.

Significant growth in AI applications for drug development over the last two decades.

Current research gaps include data quality and integration challenges.

Abstract

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.