This article investigates the prospects and challenges related to the implementation of generative neural networks in the educational processes of medical universities. The relevance of the subject matter arises from the recent integration of these technologies into academic discussions and the current absence of corresponding regulatory frameworks governing their application in education.

The study employed a qualitative approach involving in-depth interviews with both students and faculty members at the South Ural State Medical University. Thirty interviews were conducted with students, while ten additional interviews took place with professors who hold doctoral and postdoctoral qualifications.

The analysis of interview responses indicates that students frequently employ generative neural networks in their academic work, often in ethically questionable manners. Typical examples of misuse included drafting reports, thesis papers, presentations, and even completing examinations. Respondents predominantly justified their choices based on the swiftness of receiving completed outputs. Nevertheless, several rare cases of ethical uses emerged, particularly concerning statistical calculations.

Among the interviewed educators, opinions varied widely regarding the role of artificial intelligence in instruction. While most acknowledged its utility as a valuable tool, they also highlighted the importance of adhering to principles that ensure equity, transparency, and mutual respect between students, instructors, and administrators.

In conclusion, the present study emphasizes the urgent need for deeper exploration of how generative neural networks are utilized in medical education due to the risks posed by inadequate legal oversight. These findings recommend continued investigations employing quantitative techniques alongside formulating guidelines for regulatory documents aimed at safeguarding high-quality medical professional development.