TECHNOLOGIES FOR DEVELOPING DIAGNOSTIC COMPETENCE OF MEDICAL STUDENTS BASED ON A CROSS-DISCIPLINARY APPROACH (ON THE EXAMPLE OF TEACHING INFECTIOUS DISEASES)

This study explores the development of diagnostic competence among medical students through the application of cross-disciplinary educational technologies, using the teaching of infectious diseases as a model. The relevance of the research is determined by the increasing complexity of clinical diagnostics and the need for integrative knowledge in modern medical practice. A mixed-methods design was employed, involving experimental and control groups of medical students. The experimental group was trained using cross-disciplinary approaches that integrated microbiology, immunology, epidemiology, pharmacology, and clinical medicine through case-based learning, problem-based learning, simulation, and digital technologies. The results demonstrate that students exposed to cross-disciplinary instruction showed significantly higher levels of diagnostic accuracy, clinical reasoning, and decision-making efficiency compared to those trained using traditional methods. The use of simulation and artificial intelligence tools contributed to enhanced engagement, improved interpretation of clinical data, and stronger analytical skills. The study confirms that cross-disciplinary integration fosters deeper understanding and supports the development of higher-order cognitive competencies essential for effective diagnosis in infectious diseases. In conclusion, the implementation of cross-disciplinary educational technologies is an effective strategy for improving diagnostic competence in medical education. This approach can be recommended for broader integration into medical curricula to better prepare future healthcare professionals.