Medication errors are a serious issue and a preventable source of harm to patients in healthcare. This is particularly true for high-risk groups such as children, older adults, and those taking multiple medications. Traditional ways of ensuring medication safety mostly depend on reviewing past incidents and manual checks. These methods often face problems with underreporting and late detection.Recent advancements in artificial intelligence (AI) and machine learning (ML) have shifted medication safety from a reactive approach to a predictive one. This review examines AI-driven models that predict medication errors and highlights how pharmacists’ roles are changing with AI in pharmacy practice. It covers various AI techniques, including supervised and unsupervised machine learning, deep learning, and natural language processing, and how these are used in prescribing, dispensing, administration, and monitoring medication safety.The use of AI enabled clinical decision support systems (CDSS), predictive tools, and risk assessment methods has resulted in significant reductions in medication errors, improved alert relevance, and better patient safety outcomes. Pharmacists play a vital role in validating data, training algorithms, interpreting clinical data, integrating workflows, and collaborating across different professions. This ensures that AI findings lead to practical safety measures.Despite these advantages, challenges remain. Issues such as alert fatigue, data privacy, bias in algorithms, ethical decision-making responsibilities, and financial obstacles still exist. Future developments will aim to make AI easier to understand, utilize federated learning, connect with pharmacogenomics, and create systems that continuously learn. Overall, when pharmacists take the lead in adopting AI technologies, it can greatly enhance medication safety and improve patient centered pharmacy practices.
Abisha et al. (Sat,) studied this question.