The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Squarepants, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3977007 · Cited by 11,146 · cited 1×
“Blockchain was introduced by Nakamoto Satoshi for the first time in 2008 [69].”
MethodThe emergence of edge computing
Satyanarayanan, M. (2017) · Computer · doi:10.1109/mc.2017.9 · Cited by 2,832 · cited 1×
“Artificial intelligence enables machines to mimic the human brain and it learns from the experience just like a human does.”
BackgroundDwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102542 · Cited by 2,753
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655 · cited 1×
“If the metaverse‐enabling devices for example Oculus helmet and Meta Quest are attacked by hackers this may result in serious conse‐ quences [77].”
BackgroundArtificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
A survey of blockchain from the perspectives of applications, challenges, and opportunities
Monrat, A. A., Schelen, O., & Andersson, K. (2019) · IEEE Access · doi:10.1109/access.2019.2936094 · Cited by 1,077
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 · cited 1×
“6 of 19”
Blockchain for the metaverse: A Review
Huynh‐The, T., Gadekallu, T. R., Wang, W., Yenduri, G., Ranaweera, P. S., Pham, Q., da Costa, D. B., & Liyanage, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.008 · Cited by 1,053
A decentralized privacy-preserving healthcare blockchain for IoT
Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019) · Sensors · doi:10.3390/s19020326 · Cited by 881 · cited 1×
“Besides blockchain technology, a patient‑centric interaction policy also helps improve trust among patients [87,88].”
BackgroundAllam, Z., Sharifi, A., Bibri, S. E., Jones, D. S., & Krogstie, J. (2022) · Smart Cities · doi:10.3390/smartcities5030040 · Cited by 768 · cited 1×
“The researchers have in‐ vestigated the role of themetaverse in different fields such as healthcare [15], medicine [16], e‐commerce [17], education [18], and smart cities [19,20].”
Background
Flagged first, then by citations
Research works printed but not linked: 5
- Jeong, 2022, An innovative e-commerce platform incorporating metaverse to live commerce, Int. J. Innov. Comput. Inf. Control., 18, 221
- Athar, 2022, The Role of Artificial Intelligence and Blockchain in the Metaverse, 한국정보통신학회 종합학술대회 논문집, 26, 573
- Tambe, P., Cappelli, P., and Yakubovich, V. (2019, January 19–20). Role of AI in Gaming and Simulation. Proceedings of the International Conference on Computer Networks, Big Data and IoT, Madurai, India.
- Kuo, T.T., and Ohno-Machado, L. (2018). Modelchain: Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. arXiv.
- Yang, 2022, Smart Health Intelligent Healthcare Systems in the Metaverse, Artificial Intelligence, and Data Science Era, J. Organ. End User Comput., 34, 1
Cited by
Sorted by citations
Items 201–283 of 283
Rehabilitation for Parkinson's Disease Using Virtual Reality (VR)
Sathya, A., Tharini, V. J., & Aruna, A. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-5613-5.ch001
The Metaverse: An Appropriate Setting for Mental Health Care?Preprint
Gloeckler, S., Ford, T. J., & Biller‐Andorno, N. (2024) · Research Square · doi:10.21203/rs.3.rs-4571382/v1
Papadopoulou, P., & Lytras, M. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13619-1.00003-9
Enhancing Healthcare Delivery with Blockchain and Metaverse Synergy
Benaich, R., Mendili, S. E., & Gahi, Y. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-72418-3_6
Metaverse technology acceptance in Indian medical education: An SEM-based empirical investigation
Hanji, S. V., Blagov, E., Navalgund, N. R., Desai, S., & Hanji, S. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24049-2.00012-1
Metaverse evreninde tüketici davranışları üzerine yapılan çalışmaların bibliyometrik analizi
Erkuş, U., & Öztürk, A. (2024) · Business And Management Studies An International Journal · doi:10.15295/bmij.v12i4.2438
Liu, X., Cao, S., Shen, S., Li, S., Zeng, J., Zhu, H., & Zhao, Z. (2024) · International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS) · doi:10.1145/3706890.3706988
“This can greatly contribute to the progress of digital healthcare worldwide [ 5 ] .”
Shahpure, N. H., Rekha, P. M., & Varsha (2025) · Advances in computers · doi:10.1016/bs.adcom.2025.03.019
5G Speeds for Metaverse Healthcare Promises and Challenges
Savadatti, M. B., Bale, A. S., Dixit, A., & Tiwari, S. (2025) · doi:10.1002/9781394305292.ch16
Creating The Healthcare of Tomorrow
Upadhyay, V., & Sharma, S. (2025) · Wiley eBooks · doi:10.1002/9781394305292.ch5 · Cited by 1
The Role of Metaverse, AI, and 5G in Modernizing Healthcare Platforms
Chithra, R. B., Joy, S., & Bale, A. S. (2025) · doi:10.1002/9781394305292.ch6
Applicability and Integration of Blockchain in Healthcare
Teli, T. A., Mushtaq, S. U., & Bhat, W. A. (2025) · Signals and communication technology · doi:10.1007/978-3-031-88095-7_10
AI-Powered Automated Incident Response in Healthcare Cybersecurity Operations
Kotte, G. (2025) · International Journal of All Research Education & Scientific Methods · doi:10.56025/ijaresm.2025.1210243369
Artificial Intelligence and Blockchain in the Metaverse
Queiroz, M. P. C. M., Morales, A. S., Berretta, L. D. O., Filho, A. R. G., & Junior, I. G. S. (2025) · IEEE Latin Conference on IoT (LCIoT) · doi:10.1109/lciot64881.2025.11118329
Lakshmi, S., Kiruthiga, R., & Murali, S. (2025) · De Gruyter eBooks · doi:10.1515/9783111646893-010
Blockchain and the metaverse: A dual-tech approach to transforming medical data and patient care
Wani, S. R., & Afshan, R. (2025) · Intelligent Hospital · doi:10.1016/j.inhs.2025.100026
Metaverse Applications in Healthcare: SWOT Analysis
Erdal, N., Özbek, A., & Gelal, B. (2025) · İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi · doi:10.38079/igusabder.1676432
M., S., & Venkatapur, R. B. (2025) · Journal of Machine and Computing · doi:10.53759/7669/jmc202505165
“To address the issue of excessive dependence on trustworthy outsiders for electronic payments, the fundamental technology of Bitcoin depends on traditional encryptions along with distributed structure methods [15].”
A Survey on Blockchain and the Metaverse in Healthcare
Siva Subramanian, R., Venketbabu, T., Pavithra, N., Babu, R. A., Elavarasi, J., Gracious, L. A. A., & Ezhilvendan, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1225-5.ch001
Medical Diagnosis System Based on Explainable Artificial Intelligence and Blockchain
Nicolăescu, C.-G., Enescu, F. M., & Bizon, N. (2025) · Springer eBooks · doi:10.1007/978-3-031-97007-8_12
Gloeckler, S., Ford, T. J., & Biller‐Andorno, N. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1587316
Security and Privacy Concerns in AI-Infused Metaverse Solutions for Smart Healthcare Systems
Saeed, M. M. A., Mokhtar, R. A., Ahmed, Z. E., & Abdelgadir, M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-99946-8_21
Data Integration Challenges in AI-Infused Healthcare Metaverse
Adeyemo, A., Ajibola, O. S., Adeniyi, A. E., Olayiwola, S. D., Alabi, A. O., Abiodun, A. O., & Adegboye, J. O. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-99946-8_14
Definition of Metaverse in the Framework of Smart Healthcare System
Awotunde, J. B., Adeniyi, A. E., Randle, O., Abiodun, M. K., & Falola, P. B. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-99946-8_8
Metaverso in sanità: prospettive future e la sperimentazione in Toscana Sud Est
Limaj, S., D'Urso, A., D'Amato, M. G., Taddeini, F., Capitani, E., Vencia, F., Catani, V., Pianigiani, L., Salutini, E., Albino, A., et al. (2025) · MECOSAN · doi:10.3280/mesa2024-132oa20680
Sivadevuni, S. S., & R, N. J. (2025) · doi:10.1002/9781394302864.ch18
Driving Healthcare 4.0 with Intelligent Connectivity
Fowdur, T. P., Radhakeesoon, A., & Pantović, V. (2025) · Wiley eBooks · doi:10.1002/9781394302864.ch16
Metaverse and Artificial Intelligence as Web 4.0: The Next Generation of the Internet
Klarin, A., Gorenskaia, E. N., Xiao, Q., & Seet, P. (2025) · Systems Research and Behavioral Science · doi:10.1002/sres.3209 · Cited by 2
Sahu, S., & Mallick, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33789-5.00007-8
Virtual clinical and hospital in India
Mondal, H., & Mondal, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33789-5.00001-7
Singh, A., & Kaur, H. (2025) · De Gruyter eBooks · doi:10.1515/9783111612034-005
Khare, R., Sharma, Y., Singh, N., & Sijariya, R. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-666-420261014
The Use of Metaverse in Radiology: A Systematic Review
Amiri, P., Khodaveisi, T., Aslani, N., Hadideh, F., & Kornokar, R. (2025) · Health Science Reports · doi:10.1002/hsr2.71678
Ethical Issues and Data Privacy in Immersive Technologies
Evangeline, S. I. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4465-2.ch013
The Role of AI Technologies in Data Privacy and Protection in the Metaverse Era: A detailed Review
Anusha.C, Ambili, P. S., D, S. P., Surendran, A., & Margaret Mary, T. (2025) · International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE) · doi:10.1109/inspire67328.2025.11300487
1115 AI and Fuzzy Logic for Threat Detection in Healthcare Networks
Dubey, G. P., Bhujade, R. K., Asthana, S., & Arockiam, D. (2025) · De Gruyter eBooks · doi:10.1515/9783112215722-005
Virtual Immersion for Enhanced User Adoption in Ayurvedic Healthcare Using VR Technologies
S, P. G., Raghul Raj, T., Rahul, R., Siddarth, R., & Dhanapal, R. (2025) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces67310.2025.11337155
“This study addresses the problem from a computer science perspective, developing and introducing an immersive, interactive VR platform designed to simulate Ayurvedic healthcare environments for education, self-assessment, and engagement [6].”
A Narrative Review of Virtual Human Twins (VHTs) of Healthcare Professionals
Gargiulo, L., Saritas, S. N., Arar, F. E., Saitta, L., de Luca, M., Vallefuoco, E., Arlati, S., Mondellini, M., Moccaldi, N., d'Errico, G., et al. (2025) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine66377.2025.11340559
Metahealthcare: A Survey on Metaverse Through Healthcare (Preprint)Preprint
Rahimi, F., Sadeghi-Niaraki, A., Abuhmed, T., & Choi, S.-M. (2025) · doi:10.2196/preprints.81855
Jaoude, J. A., & Saadé, R. G. (2026) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2026.2626232
Healthcare in the Metaverse: Applications, Evidence, and Future Directions
Ortolan, É. V. P., de Oliveira, W. E., Lourenção, P. L. T. D. A., & do Nascimento, A. L. B. (2026) · Springer eBooks · doi:10.1007/978-3-031-89820-4_21
Kumar, S., & Sirsikar, S. (2025) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc66652.2025.11388850
“In ref [2]., Ali explored integration of explainable AI and blockchain within healthcare metaverse applications to enhance immersiveness, trust and patient data security, highlighting the transformative potential for future medical services.”
Al-Barazie, R., Zalila‐Kolsi, I., & Mohamed, A. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00281
Sahu, S., Sethi, R. C., Pattnayak, P., Mohanty, A., & Das, T. (2025) · IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST) · doi:10.1109/giest66547.2025.11387686
“The necessity to re-imagine the legal regimes governing the protection of the patient’s rights and the ethical use of the new technology is evident from the evolution towards virtual health care scenarios [4].”
Explainable & Trustworthy AI Frameworks for Healthcare with Blockchain Integration
Mahalle, V., Reddy, K. T. V., Moharle, D., & Ingole, R. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11441319
Saithong, P., & Yoddee, J. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026553
“Consistent with Ali et al. (2023) and Torous et al. (2018), participants described the JAIFULL design as “safe” and “friendly,” emphasizing the psychological benefits of aesthetic coherence.”
Privacy Meets Profit: Federated Learning in Metaverse Healthcare
Sahu, S., Mallick, N., Pattnayak, P., Sethi, R. C., Panda, P., & Mohanty, A. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11466143
Metaverse-enabled digital twins: Building intelligent and ethical healthcare systems
Sandhu, R., Channi, H. K., Kaur, S., & Vaish, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44958-1.00017-3
Anitha, M., & Sakthivel, S. R. (2026) · Research Square · doi:10.21203/rs.3.rs-8941934/v1
Manojkumar, R., Srinivas, T. A. S., Pittala, R. B., Veeramalla, S. K., Inala, K. P., & Mitra, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8889-2.ch002
Integrating service quality dimensions into Internet of Things based healthcare systems
khan, I., & Rashid, A. (2026) · International Journal of Innovation · doi:10.5585/2026.29315
Sharma, M., Swami, S., Singh, R., Gehlot, A., Thakur, A. K., Gupta, L. R., Dogra, S., & Akram, S. V. (2026) · South Asian Journal of Human Resources Management · doi:10.1177/23220937261446716
Maadallah, Y., Idrissi, Y. E. B. E., & Baddi, Y. (2026) · AIP conference proceedings · doi:10.1063/5.0318255
Integration of Blockchain and Digital Twins in Healthcare
Boopathi Raja, G. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-8177-0.ch004
Review of security threats and protection technologies in the metaverse
Xie, N., Di, X., Jiang, H., & Yang, H. (2026) · Digital Twin · doi:10.1080/27525783.2026.2680815
Abubaker, Y., Mahameed, D., Ahuja, A. S., Abukhaled, Y., Shokair, A., Boopathiraj, N., Qozat, I., Wagner, I. V., Lentz, P. C., Vasu, P., et al. (2025) · Journal of Current Ophthalmology · doi:10.4103/joco.joco_192_24
Opportunities and Challenges of Explainable AI (XAI) in Health Care: A Review
Begum, M., & Ferdush, J. (2026) · Journal of Computer and Communications · doi:10.4236/jcc.2026.146006 · Cited by 1
Architectural design requirements for metaverse-based smart healthcare systems
Ogundokun, R. O., Bello, R.-W., Owolawi, P. A., Mkoba, E., Ogbuju, E., & Olugbemi, A. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00013-7
Sharma, A. K., Sharma, T., Sharma, A. K., Verma, K. K., & Tyagi, A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00031-9
Sharma, V., Pandey, V., Verma, V., & Solanki, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00024-1
Metaverse technologies in healthcare systems
Falola, P. B., Randle, O., Adepegba, O. A., Aworinde, H. O., Adeniyi, A. E., & Awotunde, J. B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00007-1
The role of metaverse in remote health monitoring and trend forecasting
Tripathy, N., Tripathy, S. S., Bebortta, S., & Pani, S. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.10032-2
Awotunde, J. B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00008-3
Convergence of 6G, blockchain, and artificial intelligence for next-generation healthcare
Choudhry, M. D., Sundar, G. R., Sivaraj, S., Prakash, R. H., & Jeevanandham, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45875-0.00026-0
Ogundokun, R. O., Owolawi, P. A., Tu, C., Owolabi, A. O., & Das, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27692-7.00009-5
Upadhyay, L., Kale, A., Mishra, A., & Shukla, A. K. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02390-5
“It is constructed using key components such as blockchain, digital twins, artificial intelligence, and fifth-generation (5G) internet, among others [46].”
InfluentialAlahmadi, H., & Alqahtani, Y. J. (2026) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2026.2681947
Shaikh, M. F., Hassan, S. H., Shamsi, J., Maccaro, A., & Piaggio, D. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1898383
“Federated learning assistance can be found mostly in AI-integrated systems (33, 39), meaning that privacy-friendly collaborative learning is not yet developed in practical DT implementations.”
InfluentialExplainable Blockchain-Based IoT Security Using Deep Learning Models
Bharath, M. B., Pavithra, G., Hamsaveni, M., Mala, B. A., Pujeri, B., & Pasha, S. M. (2026) · International Conference on Advanced Computing and Knowledge Engineering (ICACKE) · doi:10.1109/icacke65470.2026.11651250
AI-Orchestrated Edge-Fog-Cloud Continuum for Privacy-Preserving Healthcare Intelligence
Aarthi, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0021-2.ch011
Loey, M., Krishna, V. R., Younes, O. S., Mbasso, W. F., Muniyandy, E., & Tukhtaeva, N. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70466
“Ali et al. and Tran-Dang et al. [ 3, 4, 31] suggested a metaverse healthcare platform based on blockchain, where patients and doctors met in a virtual space through avatars, and medical data were stored safely on the blockchain as explainable AI models assisted in the prediction and diagnosis of…”
Metaverse and Immersive Technologies: Redefining the Future of Healthcare: A Scoping Review
Maleki, M., Khouzani, H. R., Zackery, A., Ghoharinejhad, S., & Zarei, H. (2026) · International Journal of Preventive Medicine · doi:10.4103/ijpvm.ijpvm_335_25
AI and Machine Learning–Enabled Virtual Healthcare Ecosystems for Economic Resilience and Recovery
Singh, G. D., Ramasamy, S., Pandey, V., Sridhar, D., Gaddala, B., Vigneshwaran, K., & Bhoopathy, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7267-9.ch021
Smart healthcare systems: In the era of metaverse, artificial intelligence, and data science
Kabir, A. I., Mahim, M. W. I., Mehdi, D. A., & Vyas, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33512-9.00020-2
Blockchain and AI-Enabled Threat Detection in Distributed Network Ecosystems
Kumari, A., & Sharma, M. (2026) · Advances in wireless technologies and telecommunication book series · doi:10.4018/979-8-2600-1533-9.ch004
Trusted AI Workflow Orchestration Through Blockchain-Integrated Cloud Computing
Ali, G. A., Faraj, L. N., & Закревський, А. В. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39708-9_18
Jayashankar, K., & Reddy, V. R. S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111513
XAI for predictive modelling in electronic health records (EHRs)
Ramamurthy, C. K. V., Matheswaran, S., Paul, A. M., Mahalakshmi, M., Thayyil, B., & Vijayaraj, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45697-8.00016-5
Gayathri, D. B., & Sangeetha, D. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109434
Balaji, K. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1363-2.ch009
Bibliometric Analysis of Healthcare Metaverse
Kumar, N., Mohan, D., Neha, Upreti, S., & Bhardwaj, K. S. (2026) · doi:10.1002/9781394389629.ch16
Explainable AI in Emerging Cybersecurity Domains
Ansari, S. K., Ahmad, Y., Shajahan, N., Husain, S., Appukuttan, A., & Shahzad, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1970-2.ch008
Pesqueira, A. M. (2026) · Healthcare · doi:10.3390/healthcare14203405
“While these findings come from computational models, their application to real-world mental health scenarios suggests the potential for widespread impact if group-based interventions harness cohesive identities and strong social ties [20–23].”
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
