Novel machine learning based approach for analysing the adoption of metaverse in medical training: A UAE case study

Salloum, Said A.; Bettayeb, Anissa; Salloum, Ayham; Aburayya, Ahmad; Khadragy, Saada; Hamoudi, Rifat; Alfaisal, Raghad

Abstract

The outbreak of the COVID-19 pandemic led to disruptions in the delivery of medical training across borders, posing challenges in observing and practicing advanced surgical techniques with cutting-edge medical equipment from foreign countries. However, the utilization of educational approaches centred on the “Metaverse” concept has emerged as a promising solution to address the escalating demand for virtual medical education. Traditional technologies like Zoom video conferencing were found insufficient for comprehensive medical instruction, prompting the emergence of innovative digital teaching methodologies within the medical community of the United Arab Emirates (UAE). This study aims to investigate how students perceive the effectiveness of the Metaverse system in achieving medical training objectives in the UAE. The research employs a unique conceptual framework that links individual attributes with technological factors. By employing a blend of structural equation modelling (SEM) and machine learning (ML) methodologies, along with the analysis of importance-performance maps (IPMA), the research evaluates the factors that contribute to measuring the viability of the Metaverse system for medical training. This evaluation is conducted using data gathered from a cohort of 879 university students. The findings indicated that the OneR classifier demonstrates the highest accuracy among classifiers in forecasting users' inclination to embrace the Metaverse system for medical training, achieving an 80.7% accuracy rate. Furthermore, the study reveals a strong positive association between perceived usefulness and perceived usability, highlighting the significant impact of personal attributes and technological elements on students' decisions. Notably, individuals with a greater willingness to embrace uncertainty and innovative technologies are more inclined to use the Metaverse system for medical education. In conclusion, this multi-analytical investigation sheds light on the potential of the Metaverse system to enhance medical training and addresses the challenges posed by the COVID-19 pandemic. The findings carry important implications for the field of information systems and provide valuable insights for medical educators seeking effective solutions during times of disruption.

Citation

Salloum, S. A., Bettayeb, A., Salloum, A., Aburayya, A., Khadragy, S., Hamoudi, R., & Alfaisal, R. (in press). Novel machine learning based approach for analysing the adoption of metaverse in medical training: A UAE case study. Informatics in Medicine Unlocked, 42, 101354. https://doi.org/10.1016/j.imu.2023.101354

• Journal Article Type: Article
• Acceptance Date: Sep 10, 2023
• Online Publication Date: Sep 25, 2023
• Deposit Date: Oct 4, 2023
• Publicly Available Date: Oct 4, 2023
• Journal: Informatics in Medicine Unlocked
• Print ISSN: 2352-9148
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Pages: 101354
• DOI: https://doi.org/10.1016/j.imu.2023.101354