The future of simulation-based medical education: Adaptive simulation utilizing a deep multitask neural network

AEM Educ Train. 2021 Jul 1;5(3):e10605. doi: 10.1002/aet2.10605. eCollection 2021 Jul.


Background: In resuscitation medicine, effectively managing cognitive load in high-stakes environments has important implications for education and expertise development. There exists the potential to tailor educational experiences to an individual's cognitive processes via real-time physiologic measurement of cognitive load in simulation environments.

Objective: The goal of this research was to test a novel simulation platform that utilized artificial intelligence to deliver a medical simulation that was adaptable to a participant's measured cognitive load.

Methods: The research was conducted in 2019. Two board-certified emergency physicians and two medical students participated in a 10-minute pilot trial of a novel simulation platform. The system utilized artificial intelligence algorithms to measure cognitive load in real time via electrocardiography and galvanic skin response. In turn, modulation of simulation difficulty, determined by a participant's cognitive load, was facilitated through symptom severity changes of an augmented reality (AR) patient. A postsimulation survey assessed the participants' experience.

Results: Participants completed a simulation that successfully measured cognitive load in real time through physiological signals. The simulation difficulty was adapted to the participant's cognitive load, which was reflected in changes in the AR patient's symptoms. Participants found the novel adaptive simulation platform to be valuable in supporting their learning.

Conclusion: Our research team created a simulation platform that adapts to a participant's cognitive load in real-time. The ability to customize a medical simulation to a participant's cognitive state has potential implications for the development of expertise in resuscitation medicine.