IMPACT OF ARTIFICIAL INTELLIGENCE ALGORITHMS ON DRUG INTERACTION SCREENING AND PRESCRIPTION ERROR PREVENTION

Abstract

Artificial Intelligence (AI) has shown significant advances in healthcare, particularly as a tool to support patient safety and clinical decision-making. This study aimed to analyze the impact of AI algorithms on drug interaction screening and prescription error prevention, highlighting their contribution to clinical pharmacists’ performance in hospital settings. An integrative literature review was conducted using PubMed, Scopus, Web of Science, Embase, Virtual Health Library, and SciELO databases, including studies published between 2019 and 2026. After the selection process based on the PRISMA 2020 recommendations, 42 studies were included in the analysis. The findings demonstrated that technologies based on Machine Learning, Deep Learning, Natural Language Processing, and Clinical Decision Support Systems can analyze large volumes of data, identify drug interactions, detect prescription errors, predict adverse events, and support safer therapeutic decisions. AI contributes to reducing pharmacotherapy risks, optimizing prescription review, and strengthening clinical interventions performed by pharmacists. However, challenges such as algorithm bias, data quality, implementation costs, and the need for clinical validation remain important limitations. It is concluded that AI functions as a complementary tool in Clinical Pharmacy, enhancing analytical capacity and decision support while not replacing clinical judgment and the responsibility of healthcare professionals.