Enhancing Medical Training Through Learning From Mistakes by Interacting With an Ill-Trained Reinforcement Learning Agent.

ABSTRACT: This study presents a 3D medical simulation that employs reinforcement learning (RL) and interactive reinforcement learning (IRL) to teach and assess the procedure of donning and doffing personal protective equipment (PPE). The simulation is motivated by the need for effective, safe, and remote training techniques in medicine, particularly in light of the COVID-19 pandemic. The simulation has two modes: a tutorial mode and an assessment mode. In the tutorial mode, a computer-based, ill-trained RL agent utilizes RL to learn the correct sequence of donning the PPE by trial and error. This allows students to experience many outlier cases they might not encounter in an in-class educational model. In the assessment mode, an IRL-based method is used to evaluate how effective the participant is a

SUBMITTER: Kakdas YC 

PROVIDER: S-EPMC11486497 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature