Artificial Intelligence in Reducing Drug-Related Problems in Polypharmacy: A Systematic Review and Quantitative Evidence Synthesis

Abstract

Polypharmacy is a major global healthcare challenge associated with increased drug-related problems (DRPs), adverse drug reactions, and hospital admissions. Artificial intelligence (AI) has emerged as a promising tool to improve medication safety. To systematically evaluate the effectiveness of AI-based systems in reducing DRPs in polypharmacy patients. A PRISMA-guided systematic review was conducted using PubMed, Scopus, Web of Science, and Google Scholar (2015–2025). Thirty-eight studies were included. Both qualitative synthesis and structured quantitative statistical analysis were performed. AI-based systems reduced DRPs by 60–75%. Most studies demonstrated statistically significant improvements (p < 0.05). AI models showed strong predictive performance (AUC 0.78–0.96). AI significantly improves medication safety and reduces DRPs in polypharmacy patients. Standardization and external validation remain essential for clinical adoption.