Structuring Laboratory Classes of Artificial Intelligence in Medicine.

Abstract

This paper describes a two-year experience in designing, implementing, and restructuring an artificial intelligence in medicine course for first-year medical students. They had no prior training in computer science, mathematics, or clinical medical disciplines. The practical activities were organized into three categories: seminars (exercises, problems), hands-on practical work (initially, regressions; later, also neural networks), and video demonstrations. First-year evaluations highlighted difficulties in logic and ontologies, as well as a high variability in the quality of individual projects. In the second year, changes focused on applied work: ontology building exercises, direct comparison of simple neural networks with classical regression methods, and an introduction to Prompt Engineering. These adjustments led to a clear increase in performance and consistency of the final results. The paper supports the feasibility of early introduction of AI in medical training and the relevance of an iterative curriculum design, with a focus on conversational skills and guided applicative activity.