Beyond Sequential Teaching: A Concurrent Model for AI in Medicine

This paper describes the design and preliminary implementation of a concurrent (parallel) model for teaching AI in medicine. This elective course was addressed to residents and final year medical students aiming to integrate in each lecture the theoretical notions with practical applications. The structure of the seven two hours classes comprised a first part with main AI concepts, such as machine learning, neural networks, decision support or generative models and a second part dedicated to invited presentations on clinical or research applications. The pedagogical objective was consolidation of conceptual understanding, keeping high audience interest by immediate contextualization. Quantitative analysis of the questionnaire data indicated good internal consistency of the evaluation instrument. No significant differences were observed across professional categories or baseline AI background levels. Perceived course impact was found to be associated with both theoretical understanding and application-oriented content. These results support the appropriateness of the concurrent teaching model for AI education in medicine. Integrating theoretical foundations with practical applications within each session appears to stimulate motivation and facilitate understanding of AI principles among clinically oriented learners.