ARTIFICIAL INTELLIGENCE IN CLINICAL RESEARCH: A SYSTEMATIC LITERATURE REVIEW

Abstract

The integration of Artificial Intelligence (AI) into clinical research has grown exponentially in recent decades, keeping pace with advancements in computer science and healthcare. This systematic literature review aimed to map the main applications, potential benefits, and challenges of using AI in different stages of clinical research, including drug discovery, eligible patient selection, clinical trial design, and real-time data monitoring. The methodology followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), with searches conducted in databases such as PubMed, SciELO, and Web of Science, considering publications from 2020 to 2024. Forty-eight studies that met the inclusion criteria were selected. The results indicated that AI, particularly through machine learning and deep learning techniques, has contributed to reducing drug development time, improving the accuracy of participant screening, and enhancing patient safety. Furthermore, there was an observed increase in the use of algorithms for predictive analysis of clinical outcomes, contributing to faster and more informed decision-making. In conclusion, while AI does not replace the methodological rigor of clinical trials, it is a complementary and strategic tool for accelerating and improving the generation of knowledge in the field of health.

DOI: https://doi.org/10.56238/sevened2025.036-068