ARTIFICIAL INTELLIGENCE IN CLINICAL PHARMACOLOGY: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES
Keywords:
Artificial Intelligence, Clinical Pharmacology, Machine Learning, Precision Medicine, Pharmacogenomics, Drug Discovery..Abstract
Artificial intelligence (AI) has rapidly transformed numerous aspects of modern healthcare, including clinical pharmacology. Machine learning, deep learning, and advanced predictive analytics have enhanced drug discovery, individualized therapy, pharmacovigilance, and clinical decision-making. AI-based technologies facilitate the analysis of large biomedical datasets, allowing clinicians to optimize drug selection, improve treatment safety, and reduce adverse drug reactions. This review summarizes current evidence regarding the applications of artificial intelligence in clinical pharmacology, discusses existing challenges, and explores future perspectives. Literature published in major scientific databases was evaluated, focusing on AI-assisted drug development, precision medicine, therapeutic drug monitoring, pharmacogenomics, and medication safety.
Current evidence indicates that AI has considerable potential to improve pharmacological practice by supporting personalized medicine and optimizing therapeutic outcomes. However, issues related to data quality, algorithm transparency, ethical considerations, and regulatory approval remain significant barriers to widespread implementation
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