2021 is known as the first Year of the Metaverse, and around the world, internet giants are eager to devote themselves to it. Metaverse is the augmented virtual world formed by convergence of virtual and physical space. Users interact within this created world, meeting each other virtually, immersing themselves in performing virtual activities, which subsequently could lead to real experiences. Conventionally, the healthcare “industry” is conservative in deploying future ready technology. Demonstrating significant improvement in healthcare outcomes using the metaverse will be difficult to prove. This overview discusses the untapped potential of metaverse applications in healthcare, and also points out the advantages, disadvantages, limitations, and challenges in actual deployment of the metaverse in clinical practice in the real world. This alone will ultimately lead to the development of a business model, insurance reimbursement, and behavioral modification necessary for accepting and using a hitherto unused method in patient care.
TopThe application of metaverse in healthcare is just one example of how it is a new concept for the disruptive changes predicted in many areas of our lives. For many years, diagnosing patient problems, prescribing medical treatments, and performing surgical procedures on patients have all involved direct physical contact between patients and doctors. The introduction of telemedicine services has little altered this state (Yang et al., 2022). The recent rapid developments in technology offer limitless possibilities to improve healthcare. From virtual health to mental health and from reality management to virtual management, the metaverse has the potential to change healthcare (Kavanagh, 2021).
Although not two new initiatives, AI and data science in hospital management provide novel possibilities. The earliest artificial intelligence (AI) program, MYCIN, was utilized as early as the 1970s to assist in the treatment of blood infections (Davenport & Kalakota, 2019). The handling of a vast amount of data on patients with blood infections has been done using data science. Incorporating collected data, data science contributes in the analysis and investigation of patients' blood issues. The American Association for Artificial Intelligence (AAAI) was established in 1979 (Schrodt, 2019).
Intelligent Systems in Medicine, the first worldwide AI journal, was established in 1980. AI was used in clinical contexts between 1980 and 1990, from developing minimally-invasive procedures to exploring the idea of virtual presence during surgery (Yang et al., 2022). Automated Endoscopic System for Optimal Positioning (AESOP), a voice-activated endoscope, was introduced in 1994 with the aid of data science and machine learning techniques, enabling surgeons to view inside the bodies of the patients (Oniani et al., 2021).
The FDA recognized the implementation of algorithms to find tumors in medical photographs in 1998, despite the introduction of deep learning methods in data science. The American robotics company Computer Motion created the ZEUS Robotic Surgical System (ZRSS) in 2001 to aid in surgery. Three robotic arms on the ZRSS, including the upgraded AESOP, can be remotely operated by the surgeon (Yang et al., 2022). The Da Vinci Surgical System was created by the new business in 2003 after Computer Motion and Intuitive Surgical merged. This is not a robotic system. Scientists developed the platform to do robotically supported surgery that is minimally invasive (Morrell et al., 2021). A panel on clinical data mining, knowledge-based healthcare, and temporal data mining was organized at the Artificial Intelligence in Medicine Europe (AIME) symposium in Amsterdam, the Netherlands, in 2007 (Yang et al., 2022). Without a question, data science and AI are vital tools for the healthcare industry. Researchers in artificial intelligence in medicine (AIM) are working to create a wide range of AI-inspired approaches to address a variety of significant clinical and biological issues. Deep neural learning technology has significant potential in smart health and intelligent healthcare management, as shown by the fact that the deep neural networks introduced in 2012 have demonstrated good performance properties relative to those of more conventional AI (Yang et al., 2022).
Development in data science and AI for medical applications has significantly increased in recent years. Remote illness assessment, virtual health screening, telemedicine and many other creative and intelligent healthcare applications are emerging thanks to the use of artificial intelligence in the metaverse and data science applications in primary care (Liang & Liu, 2018). Examples of such applications include the three-dimensional immersive remote monitoring of seriously ill patients, blood glucose monitoring, clinical patient data analysis, heart rate observing, boosting capabilities for recording physical activity, and other new and previously inconceivable medical and health services (Chen & Zhang, 2022). For instance, the technology company Oculus, which Meta recently acquired, has been assisting in orthopedic surgery (Chen & Zhang, 2022).