Artificial Intelligence in Medical Education: Modelling Tuberculosis Transmission as a Teaching Tool in Phthisiology

Abstract

Modern medical education is undergoing a stage of transformation through the integration of artificial intelligence (AI) technologies. They enable personalisation of the learning process, creation of virtual clinical scenarios and simulations, and the development of critical thinking in future physicians. In teaching the discipline Phthisiology, the application of AI — particularly modelling the spread of tuberculosis—is highly relevant, as it ensures a practice-oriented approach, integration of clinical and epidemiological knowledge, and the development of interdisciplinary skills. Objective — to substantiate the feasibility and effectiveness of using AI technologies in teaching Phthisiology, with an emphasis on the application of tuberculosis transmission models as an educational tool. Materials and methods. This article is based on an analysis of contemporary scientific publications (Scopus, PubMed, Web of Science, 2018—2025) and considers the practical possibilities of applying multi-agent simulations, SEIR models, and generative AI models in the educational process. The methodological approaches include integration into lecture courses, use of virtual patients in practical classes, student involvement in independent work with simulation platforms, as well as formative and summative assessment of results. Results and discussion. A structured model for the methodological implementation of AI in teaching phthisiology is proposed, including lecture, practical, independent and assessment components. Practical case examples have been developed: simulation of tuberculosis outbreaks under migration conditions, working with virtual patients, analysis of epidemiological data and modelling the consequences of declining BCG vaccination coverage. These approaches contribute to the development of clinical reasoning, data analysis skills, digital literacy and motivation for scientific research among students. Conclusions. The use of AI in teaching Phthisiology represents a promising direction for the development of medical education. The integration of tuberculosis transmission models into the learning process promotes deeper comprehension of material, interdisciplinary integration and preparation of students for work in the context of modern evidence-based medicine. Future prospects include the creation of specialised educational platforms, the application of multimodal AI models and the development of ethical standards for their use.