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.本研究以传染病教学为示范案例，探讨跨学科教育技术在医学生诊断能力培养中的应用路径。本研究的研究价值源于临床诊断日益复杂化的趋势，以及现代医疗实践对整合性知识的迫切需求。本研究采用混合研究设计，将医学生分为实验组与对照组开展对照研究：实验组采用跨学科教学模式开展培训，该模式整合了微生物学、免疫学、流行病学、药理学与临床医学内容，并结合案例式学习（Case-Based Learning, CBL）、问题式学习（Problem-Based Learning, PBL）、模拟实训与数字技术实施教学。研究结果显示，相较于接受传统教学的学生，接受跨学科教学的学生在诊断准确率、临床推理能力与决策效率方面均有显著提升。模拟实训与人工智能（Artificial Intelligence）工具的应用，有助于提升学生的课堂参与度，优化临床数据解读能力，并强化其分析思维。本研究证实，跨学科整合能够促进学生对知识的深度理解，并助力其培养高阶认知能力——而这些能力正是开展传染病精准诊断所必需的核心素养。综上，跨学科教育技术的应用是提升医学教育中诊断能力培养效果的有效策略，该模式可推广至医学课程体系的更广泛领域，从而更好地培养未来的医疗卫生专业人才。