Medical Diagnostic Training Application
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.
Research at Western Washington University
SEP 2019 - CURRENT
Unity Game Engine