The aim of the study is to investigate if hands-on training for basic CCE with virtual reality simulators or guided by artificial intelligence is non-inferior to training by an experienced instructor.

Basic (Level 1) Critical care echocardiography (CCE) involves using an ultrasound device to qualitatively assess the heart at the bedside. It is increasingly being used at the bedside for diagnostics and screening of key differential diagnoses. Increasingly, CCE is being taught to more medical staff from many fields in medicine, including emergency medicine, anaesthesiology, intensive care medicine and even family medicine. There is a wealth of learning resources online but access to direct supervision by trainers and in-person courses is can be limited and costly. At the time of the study, one local medical school incorporated a lecture there is no credentialling pathway within local medical schools or institution. There has been increasing use of machine learning in medical imaging and deep learning algorithms are now able to guide image acquisition and allow novices with minimal training in echocardiography to obtain diagnostic-quality images. Artificial intelligence (AI) in echocardiography may improve image by novices. Ultrasound hardware that implement machine learning software in real-time can help with structure detection and identification, but more studies are needed to determine the extent that AI impacts learning.

Inclusion Criteria:

• Medical students will have limited clinical exposure to critical care echocardiography
• above the age of 21 years

Exclusion Criteria:

• prior attendance of a critical care echocardiography courses or
• refusal to participate in the study or complete both hands on sessions

local PDPA and data sharing rules