# The potential of artificial intelligence in clinical trials

## TL;DR

Artificial intelligence can improve clinical trials by making them more efficient and cost-effective, though challenges like data quality and regulation remain.

## Contribution

This review highlights emerging AI applications in clinical trials and discusses their potential and challenges.

## Key findings

- AI can optimize trial design, recruitment, and data interpretation.
- Challenges include data quality, regulatory issues, and infrastructure limitations.
- Ethical AI use requires governance frameworks with transparency and oversight.

## Abstract

Clinical trials are an important part of evidence generation in medicine but remain burdened by escalating costs, inefficiencies and manual processes. Artificial intelligence (AI) has emerged as a promising approach to address these limitations by improving efficiency across the trial lifecycle.

In this review, we examine emerging applications of AI across the clinical trial lifecycle. We highlight key examples demonstrating feasibility and potential impact.

AI‐based approaches show promise in optimizing trial design, improving recruitment, streamlining conduct and enhancing data interpretation. Despite the potential of AI in trials, challenges persist, including data quality, regulatory and privacy concerns, as well as infrastructure issues. Ethical use will require strong governance frameworks emphasizing transparency and human oversight. The success of these technologies will depend on their continuous validation and monitoring of these technologies.

With appropriate validation, monitoring and governance, AI could enable a more efficient, cost‐saving and effective clinical trial landscape that accelerates discovery.

Artificial intelligence has the potential to transform many areas of clinical trials.