(untitled) (Eureka Journal of Health Sciences & Medical Innovation, 2026)
This article is aimed at improving the methodology of teaching Biological Physics in medical higher education institutions through generative artificial intelligence-assisted virtual laboratory cases. Biological Physics occupies an important place in the formation of future physicians’ scientific worldview because it explains the physical mechanisms of biological systems, diagnostic technologies, biophysical processes in tissues, and the principles of modern medical equipment. However, many topics, including bioelectric phenomena, hemodynamics, biomechanics, optics of the eye, membrane transport, radiation biophysics, and medical imaging, are abstract and difficult for students to understand without visualization and practical modelling. The study analyzes the methodological potential of generative AI, virtual simulations, digital laboratory scenarios, and problem-based biophysical cases. On this basis, a BIOPHYS-AI methodological model was developed. The model includes problem situation analysis, AI-assisted explanation, virtual experiment, data interpretation, clinical-biophysical justification, and reflective assessment. The proposed approach contributes to the development of students’ biophysical competence, scientific reasoning, digital literacy, and the ability to apply physical laws to medical problems. The results of the methodological analysis show that the integration of generative AI and virtual laboratory cases can increase the didactic effectiveness of Biological Physics teaching when used under teacher supervision, with academic integrity, data reliability, and ethical principles being strictly observed.