journal article·2022-06-09Open access

Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System

Georgios M. Minopoulos, Vasileios A. Memos, Christos L. Stergiou, Konstantinos D. Stergiou, Andreas P. Plageras, Maria P. Koidou, Konstantinos E. Psannis

Applied Sciences· Volume 12· Issue 12

Abstract

Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: IoT and Edge/Fog Computing · COVID-19 diagnosis using AI · Internet of Things and AI

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece

Funding

Greek Ministry of Education and Religious Affairs

References (73)

Show source-provided references
1. A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system10.1016/j.future.2017.10.045 ↗
2. An IoT-aware architecture for smart healthcare systems10.1109/JIOT.2015.2417684 ↗
3. E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation10.1016/j.techfore.2018.02.018 ↗
4. Alafif, T., Tehame, A.M., Bajaba, S., Barnawi, A., and Zia, S. (2021). Machine and deep learning towards COVID-19 diagnosis and treatment: Survey, challenges, and future directions. Int. J. Environ. Res. Public Health, 18.10.3390/ijerph18031117 ↗
5. A survey on mathematical, machine learning and deep learning models for COVID-19 transmission and diagnosis10.1109/RBME.2021.3069213 ↗
6. Sevi, M., and Aydin, İ. (2020, January 26–27). COVID-19 detection using deep learning methods. Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Online.10.1109/ICDABI51230.2020.9325626 ↗
7. A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19)10.1109/ACCESS.2021.3058537 ↗
8. Zheng, Y.J., Chen, X., Song, Q., Yang, J., and Wang, L. (2022). Evolutionary Optimization of COVID-19 Vaccine Distribution with Evolutionary Demands. IEEE Trans. Evol. Comput.10.1109/TEVC.2022.3164260 ↗
9. The role of big data predictive analytics and radio frequency identification in the pharmaceutical industry10.1109/ACCESS.2018.2890551 ↗
10. Menon, A., Aishwarya, M.S., Joykutty, A.M., and Av, A.Y. (2021, January 23–25). Data Visualization and Predictive Analysis for Smart Healthcare: Tool for a Hospital. Proceedings of the 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Korea.10.1109/TENSYMP52854.2021.9550822 ↗
11. Predicting drug side effects using data analytics and the integration of multiple data sources10.1109/ACCESS.2017.2755045 ↗
12. Sarangi, S.C., and Dash, Y. (2019, January 5–6). Application of Machine Learning and Big data Analytics in Pharmacovigilance and Drug Safety. Proceedings of the 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kerala, India.10.1109/ICICICT46008.2019.8993265 ↗
13. Scanning the future of medical imaging10.1016/j.jacr.2018.09.050 ↗
14. Memos, V., Minopoulos, G., and Psannis, K. (2019, January 21–22). The Impact of IoT and 5G Technology in Telesurgery: Benefits & Limitations. Proceedings of the New Technologies in Health: Medical, Legal and Ethical Issues, Thessaloniki, Greece.
15. Jaiswal, K., Sobhanayak, S., Mohanta, B.K., and Jena, D. (2017, January 21–23). IoT-cloud based framework for patient’s data collection in smart healthcare system using raspberry-pi. Proceedings of the 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), New York, NY, USA.10.1109/ICECTA.2017.8251967 ↗
16. Deep learning fo.r multigrade brain tumor classification in smart healthcare systems: A prospective survey10.1109/TNNLS.2020.2995800 ↗
17. Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 201910.1016/j.ceh.2020.03.001 ↗
18. Maghdid, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., and Rabie, K. (2020). A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study. Cornell University. arXiv.10.1109/IRI49571.2020.00033 ↗
19. Muthukumar, S., Mary, W.S., Rajkumar, R., Dhina, R., Gayathri, J., and Mathivadhani, A. (2019, January 23–25). Smart Humidity Monitoring System for Infectious Disease Control. Proceedings of the 2019 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, Tamil Nadu, India.10.1109/ICCCI.2019.8821792 ↗
20. A deep learning approach to antibiotic discovery10.1016/j.cell.2020.01.021 ↗
21. Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review10.2174/1381612824666180607124038 ↗
22. Machine learning-driven protein engineering: A case study in computational drug discovery10.1049/enb.2019.0019 ↗
23. Momtahen, S., Al-Obaidy, F., and Mohammadi, F. (2019, January 5–8). Machine Learning with Digital Microfluidics for Drug Discovery and Development. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.10.1109/CCECE.2019.8861842 ↗
24. Bahi, M., and Batouche, M. (2018, January 24–25). Deep Learning for Ligand-Based Virtual Screening in Drug Discovery. Proceedings of the 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), Tebessa, Algeria.10.1109/PAIS.2018.8598488 ↗
25. An advanced simulator for orthopedic surgical training10.1007/s11548-017-1688-0 ↗
26. A study on big data: Issues, challenges and applications
27. Plageras, A.P., Stergiou, C., Kokkonis, G., Psannis, K.E., Ishibashi, Y., Kim, B.G., and Gupta, B.B. (2017, January 24–27). Efficient Large-scale Medical Data (eHealth Big Data) Analytics in Internet of Things. Proceedings of the 2017 IEEE 19th Conference on Business Informatics, Thessaloniki, Greece. CBI’17.10.1109/CBI.2017.3 ↗
28. Pooryousef, V., Brown, R., and Turkay, S. (2019, January 3–5). Shape Recognition and Selection in Medical Volume Visualisation with Haptic Gloves. Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OZCHI), Perth, Australia.10.1145/3369457.3369508 ↗
29. Applications of machine learning in drug discovery and development10.1038/s41573-019-0024-5 ↗
30. Internet-of-Things-Enabled Infrastructure Against Infectious Diseases10.1109/IOTM.0001.2100023 ↗
31. An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission
32. CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment10.1109/ACCESS.2020.2980938 ↗
33. Desai, M.R., and Toravi, S. (2017, January 8–9). A smart sensor interface for smart homes and heart beat monitoring using WSN in IoT environment. Proceedings of the 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India.10.1109/CTCEEC.2017.8455124 ↗
34. Plageras, A.P., Stergiou, C.L., and Psannis, K.E. (2019, January 21–22). Internet of Things for Healthcare: Challenges and Perspectives. Proceedings of the New Technologies in Health: Medical, Legal and Ethical Issues, Thessaloniki, Greece.
35. Comparative study between fiber optic and copper in communication link
36. SDN-Enabled Resource Management for Converged Fi-Wi 5G Fronthaul10.1109/JSAC.2021.3064651 ↗
37. Méndez, A. (2016). Optics in Medicine. Optics in Our Time, Springer.10.1007/978-3-319-31903-2_13 ↗
38. A tutorial on IEEE 802.11 ax high efficiency WLANs10.1109/COMST.2018.2871099 ↗
39. ITU-R (2015). IMT Vision-Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond, International Telecommunication Union.
40. Stergiou, C.L., Plageras, A.P., Psannis, K.E., and Gupta, B.B. (2020). Secure Machine Learning scenario from Big Data in Cloud Computing via Internet of Things network. Handbook of Computer Networks and Cyber Security, Springer.10.1007/978-3-030-22277-2_21 ↗
41. Alsunaidi, S.J., Almuhaideb, A.M., Ibrahim, N.M., Shaikh, F.S., Alqudaihi, K.S., Alhaidari, F.A., Khan, I.U., Aslam, N., and Alshahrani, M.S. (2021). Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors, 21.10.3390/s21072282 ↗
42. Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences
43. Impact of Artificial Intelligence in Making Better Marketing Decisions in Healthcare Industries
44. COVID-19 and artificial intelligence: Protecting health-care workers and curbing the spread10.1016/S2589-7500(20)30054-6 ↗
45. Supervised machine learning for the prediction of infection on admission to hospital: A prospective observational cohort study10.1093/jac/dky514 ↗
46. Detection of subtype blood cells using deep learning10.1016/j.cogsys.2018.08.022 ↗
47. Identification of COVID-19 can be Quicker through Artificial Intelligence Framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns are Under Quarantine
48. Wang, Y., Hu, M., Li, Q., Zhang, X.P., Zhai, G., and Yao, N. (2020). Abnormal Respiratory Patterns Classifier may Contribute to Large-scale Screening of People Infected with COVID-19 in an Accurate and Unobtrusive Manner. Cornell University. arXiv.
49. Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., and Shen, D. (2020). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. Cornell University. arXiv.10.1109/RBME.2020.2987975 ↗
50. Epidemic Prediction
51. Punn, N.S., Sonbhadra, S.K., and Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms. medRxiv, 1–10.10.1101/2020.04.08.20057679 ↗
52. The rise of deep learning in drug discovery10.1016/j.drudis.2018.01.039 ↗
53. Kim, H., Lim, Y., Seo, S., Lee, K., Kim, J., and Shin, W. (2021). A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer. Appl. Sci., 11.10.3390/app11136194 ↗
54. Lampos, V., Zou, B., and Cox, I.J. (2017, January 3–7). Enhancing feature selection using word embeddings: The case of flu surveillance. Proceedings of the 26th International Conference on World Wide Web (WWW), Perth, Australia.10.1145/3038912.3052622 ↗
55. Long, M. (2022, February 20). Deep Learning in Healthcare: How It’s Changing the Game. Available online: www.aidoc.com/blog/deep-learning-in-healthcare.
56. Tirado-Martin, P., and Sanchez-Reillo, R. (2021). BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets. Appl. Sci., 11.10.3390/app11135880 ↗
57. A Survey on Haptic Data Over 5G Networks
58. Crandall, R.E. (2022, January 14). Positive Human-Robot Relationships Will Power the Future Supply Chain Workforce. Available online: www.supplychain247.com/article/positive_human_robot_relationships_will_power_the_future_workforce/robotics.
59. Combating COVID-19—The role of robotics in managing public health and infectious diseases10.1126/scirobotics.abb5589 ↗
60. Design strategy and issues of the Delthaptic, a new 6-DOF parallel haptic device10.1016/j.mechmachtheory.2018.06.015 ↗
61. Chizeck, H.J., Ryden, F., and Stewart, A. (2017). Methods and Systems for Six-Degree-of-Freedom Haptic Interaction with Streaming Point Data. (No. 9,753,542), U.S. Patent.
62. Ateya, A.A., Muthanna, A., Vybornova, A., Gudkova, I., Gaidamaka, Y., Abuarqoub, A., and Koucheryavy, A. (2019). Model mediation to overcome light limitations—Toward a secure tactile Internet system. J. Sens. Actuator Netw., 8.10.3390/jsan8010006 ↗
63. A new head-mounted display-based augmented reality system in neurosurgical oncology: A study on phantom10.1080/24699322.2017.1358400 ↗
64. Izard, S.G., Plaza, Ó.A., Torres, R.S., Méndez, J.A.J., and García-Peñalvo, F.J. (2019, January 16–18). NextMed, Augmented and Virtual Reality platform for 3D medical imaging visualization: Explanation of the software platform developed for 3D models visualization related with medical images using Augmented and Virtual Reality technology. Proceedings of the 7th International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM), Leon, Spain.10.1145/3362789.3362936 ↗
65. Douglas, D.B., Wilke, C.A., Gibson, J.D., Boone, J.M., and Wintermark, M. (2017). Augmented reality: Advances in diagnostic imaging. Multimodal Technol. Interact., 1.10.3390/mti1040029 ↗
66. Gleb, B. (2022, January 28). VR vs. AR vs. MR: Differences and Real-Life Applications. Available online: https://rubygarage.org/blog/difference-between-ar-vr-mr.
67. Gallagher, L., and Alford, J. (2022, March 01). Mixed-Reality Headsets in Hospitals Help Protect Doctors and Reduce Need for PPE. Imperial College London. Available online: www.imperial.ac.uk/news/197617/mixed-reality-headsets-hospitals-help-protect-doctors/.
68. A Lightweight XMPP Publish/Subscribe Scheme for Resource-Constrained IoT Devices10.1109/ACCESS.2017.2742020 ↗
69. Psannis, Manos Roumeliotis and Dan Schonfeld, Real-time wireless multisensory smart surveillance with 3D-HEVC streams for internet-of-things (IoT)10.1007/s11227-016-1769-9 ↗
70. Efficient Redundant Frames Encoding Algorithm for Streaming Video over Error Prone Wireless Channels10.1587/elex.6.1497 ↗
71. Efficient Flexible Macroblock Ordering Technique10.1093/ietcom/e91-b.8.2692 ↗
72. Robust network-based analysis of the associations between (epi) genetic measurements10.1016/j.jmva.2018.06.009 ↗
73. Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis10.1002/gepi.22194 ↗