THE UTILIZATION OF ARTIFICIAL INTELLIGENCE IN IMPROVING MEDICATION SAFETY: A SYSTEMATIC REVIEW OF CLINICAL PHARMACY SERVICES
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This study aims to analyze the use of artificial intelligence (AI) to improve treatment safety in clinical pharmaceutical services. The method used is a systematic literature review conducted in accordance with the PRISMA 2020 guidelines. Article searches were conducted in the Scopus, PubMed, Web of Science, and ScienceDirect databases for publications from 2020–2026 using the keywords artificial intelligence, machine learning, medication safety, medication error, adverse drug event, and clinical pharmacy. Articles were selected based on inclusion and exclusion criteria, then analyzed using narrative synthesis and thematic analysis. The results of the study show that AI has been used in prescription review, medication error prediction, drug interaction detection, side effect identification, clinical alert optimization, drug reconciliation, and prioritization of high-risk patients. Machine learning, deep learning, natural language processing, and clinical decision support systems can increase the speed of data analysis, the accuracy of risk identification, and the efficiency of pharmacists' work. However, most models still use retrospective data from a single institution, have not undergone external validation, and have limitations in algorithmic transparency, data quality, outcome generalization, patient privacy, and potential bias. These findings confirm that AI should be used as a decision-support tool with a human-in-the-loop approach, rather than as a substitute for pharmacists' professional judgment. Secure implementation requires local validation, periodic audits, integration with electronic medical records, improvement of pharmacists' digital competencies, and clear regulations. Further research needs to test the effectiveness of AI through multicenter prospective studies with measurable clinical, economic, and patient safety outcomes. Overall, AI has the potential to shape clinical pharmacy services that are more predictive, personalized, responsive, and oriented towards the prevention of adverse drug events on an ongoing basis at various levels of healthcare
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