(untitled) (Eureka Journal of Health Sciences & Medical Innovation, 2026)
This article presents a methodological approach to developing medical students’ digital-clinical competence through generative artificial intelligence-based clinical cases in the course “Information Technologies in Medicine.” The rapid digitalization of health care, the expansion of electronic medical records, telemedicine, clinical decision-support systems, and generative AI tools require future physicians to combine technological literacy with clinical reasoning, evidence verification, ethical responsibility, and data-protection skills. The study is methodological and analytical in nature. It is based on a review of regulatory documents, international recommendations, and recent research on artificial intelligence in medical education, followed by pedagogical modelling and clinical-case design. The study proposes the CLINIC-AI model, which includes clinical problem formulation, learning-objective definition, information anonymization, guided interaction with generative AI, independent verification,and clinical reflection. The model positions AI as an educational instrument for analysis rather than as a substitute for medical judgement. Four types of educational cases are described: electronic health-record analysis, telemedicine triage, identification of AI hallucinations, and protection of confidential medical information. Assessment indicators are proposed for information management, clinical application, AI literacy, evidence-based verification, ethical decision-making, and reflective communication. The methodology can be used in practical lessons to connect information technologies with clinically relevant tasks and to prepare students for responsible use of AI in future professional activity.