AIGen Club – The use of cognitive modes as a tool to learn clinical reasoning in medicine
“The learning process of medical practice has specific characteristics that differ from other professions, namely the process of clinical reasoning and decision. This process progresses from the acquisition of knowledge in biomedical sciences (e.g. biochemistry, anatomy, physiology, etc.), that is mainly factual and deterministic in nature, through the application of knowledge to the clinical sciences (e.g. cardiology, surgery, etc.), typically involving decision making relating to diagnostics and therapeutics, of a more probabilistic nature. This cognitive process, commonly designated as “clinical reasoning”, is challenging to the medical student that, when combined with the study workload, is a great source of discomfort and insecurity. This is particularly relevant during the assessment process since the degree of uncertainty that clinical decisions implicate generate frustration and suspicion on the assessment and student’s self-confidence.
Recent studies reported the use of Automated Item Generation (AIG) as a tool to develop assessment items that test the application of clinical reasoning. AIG use in medical education is more recent and is based on the development of cognitive models that help deconstruct clinical reasoning and organize it in cognitive schemes (algorithms) of the decision-making process.
The AIGen Club project aims to create a forum of discussion among faculty and students where the process of clinical decision-making is explored resorting to the development of cognitive models. It is intended that students develop their clinical reasoning skills by developing cognitive models for the most common clinical conditions. This forum will work in a hybrid environment with virtual and face to face sessions. The secondary purpose of project is to build a bank of cognitive models that allows the development of automated items that could be used both in formative and summative assessments. This process will train students with cognitive models that will be used in summative assessment increasing their motivation to study and allowing for a causality nexus between the acquisition of knowledge and its application during assessment.
Main Project Outcomes
S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.
“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.
Main Project Outcomes
this project has generated 1 book chapter
A new AIG module has been developed for QuizOne