artificial intelligence in managing and reducing medication errors: a systematic review

Background: in studies examining adverse events in hospitals, medication errors were identified as the primary or contributing factor in nearly one out of every five incidents. research has shown that artificial intelligence and machine learning algorithms can assist doctors in making more accurate diagnoses and outperform human practitioners in predicting certain medical outcomes. reducing medication errors (mes) is most crucial in three areas: electronic prescriptions, medication error surveillance, and barcode medication administration systems. this systematic review examines the role and applications of artificial intelligence in the management and reduction of medication errors. methods: searches were conducted for randomized clinical trials in english on pubmed, web of science, scopus, science direct and ieee xplore, from inception to 2024/9/18. also, the google scholar search engine has been reviewed. risk of bias and quality were assessed with the cochrane risk-of-bias (rob) 2.0 tool. the review followed prisma 2020 (preferred reporting items for systematic reviews and meta-analysis) guidelines (fig.1). the protocol has been registered in prospero by code: crd42024590942 results: the search strategy identified a total of 45824 articles, of which 19 articles were included in the review. in these studies, five areas were included: education and learning, quality improvement, medication error prediction, medication error detection, and medication error management. conclusion: this systematic review shows that ai significantly reduces medication errors by improving prediction, detection, and management. it enhances safety and efficiency but still faces challenges in privacy, ethics, and system integration.