APPLICATION OF ARTIFICIAL INTELLIGENCE IN CLINICAL PHARMACOLOGY (LITERATURE REVEW)

Background. Clinical pharmacology is a scientific discipline that covers all aspects of the interaction between drugs and humans. Aim: to review current data on the possibility of using artificial intelligence (AI) to optimize scientific research, practical application and education in clinical pharmacology. Materials and methods. Analysis of data presented in PubMed, using the keywords "clinical pharmacology", "artificial intelligence". Results. It was found that AI is used at all stages of drug development from molecule discovery to real clinical practice. In particular, AI provides prediction of the pharmacokinetics and pharmacodynamics of drugs, the need for monitoring therapy, and the risk of drug interactions. The introduction of AI-based programs (for example, ChatGPT) into the activities of modern clinics allows to increase the accuracy of prescriptions (up to 50%), reduce repeat hospitalizations (up to 58%) and reduce administrative costs. AI models allow to individualize the dose, in particular for drugs with a narrow therapeutic index (for example, vancomycin); determine the dose taking into account pharmacogenetic reactions; accurately identify the risk of drug interactions and offer clinical solutions. In the educational sphere, the use of ChatGPT allows to simulate clinical situations, accelerating the creation of cases and test tasks (although almost 30% of the generated tasks were unsuitable without expert revision); promotes the development of clinical thinking in students; forms decision-making skills. Among the problems of using AI in clinical pharmacology, in addition to the need for expert assessment of data quality, issues of ethics, confidentiality and dual use of technologies (for example, the risks of developing bioweapons) are of great importance, therefore it is important to combine the capabilities of AI with the experience of specialists. Conclusion. Artificial intelligence has great potential for organizing clinical trials; optimizing the therapeutic effects of drugs, taking into account the risk of pharmacogenetic reactions and drug interactions, improving clinical decision-making and patient safety; and learning personalized drug use. It is important to use AI tools in conjunction with expert judgment from healthcare professionals, and to properly consider regulatory considerations, data privacy, and ethical implications.