The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Open accessAbstract
The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.
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Cites
A Metaverse: Taxonomy, Components, Applications, and Open Challenges
Park, S.-M., & Kim, Y.-G. (2022) · IEEE Access · doi:10.1109/access.2021.3140175 · Cited by 1,824
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,575
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · cited 1×
“Artificial intelligence (AI), also known as machine intelligence, focuses on the development and management of technology that can autonomously learn to make decisions and carry out tasks on behalf of humans [90].”
BackgroundDeep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406
Cloud Computing: Issues and Challenges
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An Overview on Edge Computing Research
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An IoT-Aware Architecture for Smart Healthcare Systems
Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2417684 · Cited by 1,167
A survey of blockchain from the perspectives of applications, challenges, and opportunities
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Educational Applications of Metaverse: Possibilities and Limitations
Kye, B., Han, N., Kim, E., Park, Y., & Jo, S. (2021) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2021.18.32 · Cited by 1,076
Leveraging Digital Twin Technology in Model-Based Systems Engineering
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“also mentioned by Michael Grieves in the year 2002 [140].”
Background
Cited by
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Blockchain for the metaverse: A Review
Huynh-The, T., Gadekallu, T. R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.-V., da Costa, D. B., & Liyanage, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.008 · Cited by 771
Self-citationUllah, H., Manickam, S., Obaidat, M., Laghari, S. U. A., & Uddin, M. (2023) · IEEE Access · doi:10.1109/access.2023.3286696 · Cited by 198
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183
Self-citationExplainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170
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Metadata
- Authors
- Chengoden, R., Victor, N., Huynh-The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R.
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 12765-12795
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4319986951
- Cited by
- 508
- References
- 170
- FWCI
- 86.23
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chengoden, R., Victor, N., Huynh-The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R. (2023). Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions. IEEE Access. https://doi.org/10.1109/access.2023.3241628
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
