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Application and Challenges of Blockchain in IoMT in Smart Healthcare System

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Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT)

Abstract

The paradigm of smart healthcare has increasingly gained prominence as information technology has advanced. Smart healthcare employs a new generation of technological advancement, including the Internet of Medical Things (loMT), big data, cloud service, Machine Learning (ML), and Artificial Intelligence (AI), to completely alter the existing health service, healthcare access more affordable, easy, and customized. This book chapter mostly explored the issues of smart healthcare systems implemented using IoMT, its advantages of it, the structural and functional components of IoMT, and their challenges to present the concept of smart healthcare.

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References

  1. P. Chithaluru, S. Kumar, A. Singh, A. Benslimane, and S. K. Jangir. “An Energy-Efficient Routing Scheduling Based on Fuzzy Ranking Scheme for Internet of Things.” IEEE Internet of Things Journal 9, no. 10, pp. 7251–7260, 2021.

    Article  Google Scholar 

  2. Martin JL, Varilly H, Cohn J, Wightwick GR. Preface: technologies for a smarter planet. IBM J Res Dev 2010;54(4):1–2.

    Article  Google Scholar 

  3. Gong FF, Sun XZ, Lin J, Gu XD. Primary exploration in the establishment of China's intelligent medical treatment. Mod Hos Manag 2013;11(02):28–9.

    Google Scholar 

  4. Pan F. Health care is an area where information technology plays an important role: an interview with Wu He-Quan, a member of the Chinese Academy of Engineering. China Med Herald 2019;16(3):1–3.

    Google Scholar 

  5. Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K. Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. FuturGener Comput Syst 2018;78(part 2):659–76.

    Article  Google Scholar 

  6. S. K. Ramakuri, P. Chithaluru, and S. Kumar. “Eyeblink robot control using brain-computer interface for healthcare applications.” International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics (IJMDWTFE) 10, no. 2, 38–50, 2019.

    Article  Google Scholar 

  7. Polat K, Gunes S. Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer. Expert Syst Appl 2008;34(1):214–21.

    Article  Google Scholar 

  8. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7638):115–8.

    Article  Google Scholar 

  9. Wang SJ, Summers RM. Machine learning and radiology. Med Image Anal 2012;16(5): 933–51.

    Article  Google Scholar 

  10. High R. The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works. New York, N.Y.: IBM WATSON. 2012, http://www.redbooks.ibm.com/redpapers/pdfs/redp4955.pdf. Accessed March 20, 2019.

  11. Qi RJ, Lyu WT. The role and challenges of artificial intelligence-assisted diagnostic technology in the medical field. Chin Med Device Inf 2018;24(16):27–8.

    Google Scholar 

  12. Somashekhar SP, Sepulveda MJ, Puglielli S, et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol 2018;29(2):418–23.

    Article  Google Scholar 

  13. Wang WD, Lang JY. Reflection and prospect: precise radiation therapy based on bionomics/radionics and artificial intelligence technology. Chin J Clin Oncol 2018;45(12): 604–8.

    Google Scholar 

  14. Peters BS, Armijo PR, Krause C, Choudhury SA, Oleynikov D. Review of emerging surgical robotic technology. Surg Endosc 2018;32(4):1636–55.

    Article  Google Scholar 

  15. Ye ZW, Wu XH. The latest application progress of mixed reality technology in orthopedics. J Clin Surg 2018;26(1):13–4.

    Google Scholar 

  16. Merck SF. Chronic disease and mobile technology: an innovative tool for clinicians. Nurs Forum 2017;52(4):298–305.

    Article  Google Scholar 

  17. Willard-Grace R, DeVore D, Chen EH, Hessler D, Bodenheimer T, Thom DH. The effectiveness of medical assistant health coaching for low-income patients with uncontrolled diabetes, hypertension, and hyperlipidemia: protocol for a randomized controlled trial and baseline characteristics of the study population. Bmc Fam Pract 2013;14:27.

    Article  Google Scholar 

  18. Andreu-Perez J, Leff DR, Ip HMD, Yang GZ. From wearable sensors to smart implants toward pervasive and personalized healthcare. IEEE Trans Biomed Eng 2015;62(12): 2750–62.

    Article  Google Scholar 

  19. Zhang DM, Liu QJ. Biosensors and bioelectronics on smartphones for portable biochemical detection. Biosens Bioelectron 2016;75:273–84.

    Article  Google Scholar 

  20. Chan M, Campo E, Esteve D, Fourniols JY. Smart homes – current features and future perspectives. Maturitas 2009;64(2):90–7.

    Article  Google Scholar 

  21. Liu L, Stroulia E, Nikolaidis I, Miguel-Cruz A, Rios Rincon A. Smart homes and home health monitoring technologies for older adults: a systematic review. Int J Med Inform 2016;91:44–59.

    Article  Google Scholar 

  22. Akmandor AO, Jha NK. Keep the stress away with SoDA: stress detection and alleviation system. IEEE Trans Multi-Scale Comput Syst 2017;3(4):269–82.

    Article  Google Scholar 

  23. Yin HX, Jha NK. A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles. IEEE Trans Multi-Scale Comput Syst 2017;3(4):228–41.

    Article  Google Scholar 

  24. Estrin D, Sim I. Open mHealth architecture: an engine for health care innovation. Science 2010;330(6005):759–60.

    Article  Google Scholar 

  25. F. Pandey, S. Gupta, and S. Kumar. “Information hiding using image steganography – A survey.” Journal of Basic and Applied Engineering Research (JBAER) 14, 2014.

    Google Scholar 

  26. Redfern J. Smart health and innovation: facilitating health-related behavior change. Proc Nutr Soc 2017;76(3):328–32.

    Article  Google Scholar 

  27. Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell 2015;163(5):1079–94.

    Article  Google Scholar 

  28. White RW. Skill discovery in virtual assistants. Commun ACM 2018;61(11):106–13.

    Article  Google Scholar 

  29. Ortiz CL. Holistic conversational assistants. Ai Mag 2018;39(1):88–90.

    Google Scholar 

  30. Yang PJ, Fu WT. Mindbot: a social-based medical virtual assistant. 2016 IEEE International Conference on Healthcare Informatics (ICHI). New York, N.Y.: IEEE. 2016, https://www.onacademic.com/detail/journal_1000039757790210_abfe.html. Accessed March 20, 2019.

  31. Zhang JZ, Li YK, Cao LY, Zhang Y. Research on the construction of smart hospitals at home and abroad. Chin Hos Manag 2018;38(12):64–6.

    Google Scholar 

  32. Li K, Wang J, Li T, Dou FX, He KL. Application of the internet of things in supplies logistics of the intelligent hospital. Chin Med Equipment 2018;15(11):172–6.

    Google Scholar 

  33. Álvarez López Y, Franssen J, Álvarez Narciandi G, Pagnozzi J, González-Pinto ArrillagaI, Las-Heras Andrés F. RFID technology for management and tracking: e-Health applications. Sensors (Basel) 2018;18(8) pii:E2663.

    Article  Google Scholar 

  34. Demirkan H. A smart healthcare systems framework. IT Professional 2013;15(5):38–45.

    Article  Google Scholar 

  35. Chen Q, Lu Y. Construction and application effect evaluation of integrated management platform of intelligent hospitals is based on big data analysis. Chin Med Herald 2018;15(35): 161–4, 172.

    Google Scholar 

  36. Bakkar N, Kovalik T, Lorenzini I, et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol 2018;135(2):227–47.

    Article  Google Scholar 

  37. No authors listed. Oncologists partner with Watson on genomics. Cancer Discov 2015;5 (8):788.

    Article  Google Scholar 

  38. Liu JT, Liu YH. Application of computer molecular simulation technology and artificial intelligence in drug development. Technol Innov Appl 2018(2):46–7.

    Google Scholar 

  39. Geller NL, Kim DY, Tian X. Smart technology in lung disease clinical trials. Chest 2016;149(1):22–6.

    Article  Google Scholar 

  40. Nugent T, Upton D, Cimpoesu M. Improving data transparency in clinical trials using blockchain smart contracts. F1000 Res 2016;5:2541.

    Article  Google Scholar 

  41. Kamel Boulos MN, Wilson JT, Clauson KA. Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. Int J Health Geogr 2018;17(1):25.

    Article  Google Scholar 

  42. Xiang GY, Zeng Z, Shen YJ. Present situation and development trend of China's intelligent medical construction. Chin Gen Pract 2016;19(24):2998–3000.

    Google Scholar 

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Gupta, S., Sharma, H.K., Kapoor, M. (2023). Application and Challenges of Blockchain in IoMT in Smart Healthcare System. In: Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT) . Springer, Cham. https://doi.org/10.1007/978-3-031-18896-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-18896-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18895-4

  • Online ISBN: 978-3-031-18896-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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