Smart Learning: An Interactive Dissection Simulator for Medical Students Through Virtual Reality

Author:

Jamal Sheikh Asad1,Khan Alyan2,Shah Samiullah2,Siddiqui Shayan Waris2,Khan Sallar2

Affiliation:

1. Aga Khan University Hospital

2. Sir Syed University of Engineering and Technology

Abstract

Abstract Virtual reality is not just a new technology but also a dominating one since it provides an immersive environment and simulation. It is being adopted in multiple fields for its use to train people with their related fieldwork. As a result, it is now quite the general and easily accessible technology for people to enjoy the 3D immersive environment. Moreover, Facebook's plan to introduce the metaverse has caused immense development in this field. Its interactable environment is also being adopted in the teaching platforms to provide students with the best possible learning. As a result of its simulation and learning, it has also made its way to many simulation fields such as Heavy Transportation, Airforce, and Space. Especially in medical education to teach the field worker about complicated surgeries and human anatomy, and its widely used for demonstration and learning. Many restrictions in medical education are being overcome by Virtual reality since the dissection of the corpse has been terminated from the real world for experimentation and learning anatomy for medical students is becoming a problem. Many alternatives are explored to provide better anatomy learning; the best physical 3D simulation models are present, yet not all institutions can sustain the cost and the place for such alternatives. Because of this, it is becoming a more significant obstacle to providing a better learning environment for the students. This research is for the medical field, providing a virtual reality dissection system application on oculus quest 2. This application provides the basic and essential human anatomy that a student or a doctor can practice in the realistic virtual world. The 3D models used in this application are close to realism for detailed anatomy learning, as shown in Fig [8].

Publisher

Research Square Platform LLC

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