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Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security
Open accessAbstract
Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.
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“The authors of [16] envisioned a healthcare metaverse where AI and blockchain work together to offer secure and efficient remote health monitoring and virtual consultations.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [16]
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Cited by
Sorted by citations
Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567
“Homomorphic Encryption in healthcare AI systems enables secure data utilization, ensuring patient confidentiality is maintained even during complex data analysis processes [15].”
BackgroundDwivedi, Y. K., Kshetri, N., Hughes, L., Rana, N. P., Baabdullah, A. M., Kar, A. K., Koohang, A. A., Navarrete, S. R., Belei, N., Balakrishnan, J., et al. (2023) · Information Systems Frontiers · doi:10.1007/s10796-023-10400-x · Cited by 268
“A centralised information flow using blockchains could be developed (Ali et al., 2023), enabling rigorous identity markers integrated with citizenship identities, and authorising digital currencies.”
MethodUllah, H., Manickam, S., Obaidat, M., Laghari, S. U. A., & Uddin, M. (2023) · IEEE Access · doi:10.1109/access.2023.3286696 · Cited by 198
“Reference [10] advocates for the fusion of AI and blockchain in the metaverse for superior healthcare services, but the study lacks a practical demonstration of such a system.”
BackgroundUnlocking the power of blockchain in education: An overview of innovations and outcomes
Koshiry, A. M. E., Eliwa, E. H. I., El‐Hafeez, T. A., & Shams, M. Y. (2023) · Blockchain Research and Applications · doi:10.1016/j.bcra.2023.100165 · Cited by 168
Mozumder, M. A. I., Armand, T. P. T., Uddin, S. M. I., Athar, A., Sumon, R. I., Hussain, A., & Kim, H. (2023) · Applied Sciences · doi:10.3390/app13085127 · Cited by 106
Self-citation
Metadata
- Authors
- Ali, S., Abdullah, Armand, T. P. T., Athar, A., Hussain, A., Ali, M., Yaseen, M., Joo, M.-I., & Kim, H.-C.
- Affiliations
- Inje University (KR)
- Corresponding author
- Joo, Moon-Il
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-01-04
- Volume
- 23
- Issue
- 2
- Pages
- 565
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9862285
- OpenAlex
- W4313585122
- Cited by
- 285
- References
- 82
- Citations by year
- 2026: 49 · 2025: 96 · 2024: 84 · 2023: 55
- FWCI
- 66.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Korean Government (Ministry of Science and ICT) (1711139492, 2021R1I1A1A01050306)
- National Research Foundation of Korea (NRF) (1711139492, 2021R1I1A1A01050306)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- metaverse, health care, blockchain, explainable AI, patient data security, disease prediction, disease diagnosis, Grad-CAM, lime, avatars, data privacy, trust in healthcare
- MeSH terms
- Blockchain, Artificial Intelligence, Delivery of Health Care, Humans, Computer Security, Trust
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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