Medical Diagnostic Training Application


About

Medical students need extensive training before going into the field and medical professionals often need ongoing training throughout their careers. This training is crucial to a medical professional’s career and gives medical students the proper experience to be able to treat real people with varying conditions. However, that training must be extensive and varied to be effective, it requires proper equipment and it needs to be carried out by highly trained domain specialists and qualified instructors which results in this training being expensive and less accessible to training programs with low budgets. This has pushed the development of scenario-based training. By utilizing multiple aspects of simulation, scenario-based training is designed to provide an experience that effectively mimics a real situation. This provides the most convincing experience but, due to its complexity, it is expensive and time-consuming to develop. There is value in an automated solution that can rapidly produce quality medical scenarios for virtual training. Virtual training offers the advantages of self-directed learning, facilitates trainees developing skills at their own pace, provides opportunities for unlimited repetition in a safe learning environment. The question we aim to solve is: Is procedural generation practical for medical training applications and does it offer any benefit over randomized content.

NOTE: This is still being actively worked on as a part of my research at Western Washington University.

For

Research at Western Washington University

Date

SEP 2019 - CURRENT

Tools Used

Unity Game Engine

Unity Collab

Blender

Visual Studio