Utilizing AI and big data to predict and prevent adverse drug reactions

Adverse drug reactions (ADRs) remain a major challenge in clinical pharmacology, contributing significantly to patient morbidity and mortality. Traditional methods of ADR detection, such as spontaneous reporting systems (SRS) and clinical trials, often fail to identify ADRs in real-time, limiting their effectiveness. However, the advent of Artificial Intelligence (AI) and Big Data analytics has opened new avenues for improving ADR prediction and prevention. By leveraging vast amounts of clinical, genomic, and pharmacological data, AI models, particularly machine learning (ML) and deep learning, offer powerful tools for predicting ADRs with high accuracy. This review explores the application of AI and Big Data technologies in ADR prediction, detailing the methodologies, data sources, and models used, as well as challenges and opportunities for their future implementation. Furthermore, the integration of real-time data and AI-based algorithms into clinical practice could enhance pharmacovigilance systems, leading to safer drug prescriptions and better patient outcomes.