Abstract
The aim of this study is to identify the applications of artificial intelligence (AI) in the administration of medications and health treatments, with the goal of highlighting its primary uses in therapeutic personalization and describing its contributions to clinical optimization. An exploratory review was conducted using the PRISMA-ScR method, which included searches in high-impact international databases (Scopus and PubMed) and the application of eligibility criteria. The aspects evaluated included the year of publication, the author, the title, the journal and the abstract. This process resulted in the inclusion of a total of 13 articles, which were examined through a thematic analysis, allowing for the identification of emerging categories related to predictive models for the detection of drug interactions, dose adjustment, prevention of medication errors, therapeutic monitoring and decision-making support. There are still gaps in the literature related to data quality and representativeness, healthcare system interoperability, algorithmic governance and the definition of institutional responsibilities. Consequently, the sustainable consolidation of AI in the administration of medicines and health treatments will depend on the convergence of prospective clinical validation, the development of specific ethical and regulatory frameworks, the strengthening of interoperable infrastructures and the preservation of human clinical judgement in hybrid decision-making models.
Keywords: Artificial intelligence, Applications, Drugs, Healthcare treatments, Medicine.