TRAINING MEDICAL PERSONNEL BASED ON ARTIFICIAL INTELLIGENCE AND MODERN APPROACHES

• Yuldasheva Zarofat Igamberdievna

  Candidate of Medical Sciences Department of General Medicine Faculty of Medical Work Angren University

  Author

• Raxmatullaeva Maxfuza Igamberdievna

  Senior Lecturer. Department of Professional Sciences. Faculty of Therapeutic Work Angren University

  Author

This article examines how artificial intelligence and modern educational approaches can be integrated to strengthen the preparation of medical personnel in Uzbekistan’s medical-university context. The core argument is that AI should not be treated as a standalone “technology module,” but as an enabling layer embedded across the entire training continuum: preclinical knowledge acquisition, clinical reasoning, skills formation, assessment, and continuous professional development. The paper conceptualizes training quality through a competency-based lens that prioritizes patient safety, diagnostic accuracy, communication, teamwork, evidence-informed decision making, and ethical conduct. Within this framework, AI is analyzed as a set of tools and methods— adaptive learning systems, clinical decision-support simulations, automated feedback for procedural skills, analytics for curriculum optimization, and naturallanguage interfaces for academic writing and case-based learning—whose educational impact depends on governance, pedagogical design, and faculty readiness. The study highlights key implementation conditions for Uzbekistan: alignment with national health-system priorities, institutional data stewardship, local-language and locally relevant clinical content, infrastructure and interoperability in teaching hospitals, and clear boundaries between educational support and clinical responsibility. The expected outcomes include improved personalization of learning trajectories, earlier identification of gaps in competence, better standardization of assessment, and more efficient use of clinical training time. The article concludes that successful adoption requires a balanced model that combines AI-enabled learning with human supervision, structured reflection, and rigorous evaluation of learning outcomes, ensuring that technological innovation translates into measurable professional competence and safe clinical practice.

• pdf

This work is licensed under a Creative Commons Attribution 4.0 International License.