EMERGING TRENDS IN HEALTHCARE AND MEDICAL TECHNOLOGY

Main Article Content

Laila Abdullah Ali Alzain1, Nouf Abdullah Alfraiji2, Thana Mohammed Aldhahri3, Raffah Mahdi Bajudah4, Abdullah Ali Nasser Oqdi5, Osama Ali Yahia Akkam6, Ahmed Yahya Jabber Aqeli7, Nawaf Abdulrahman Abdullah Almubireek8, Saleh Mohammed Edan Alzahrani9, Mohammed Saad Alshahrani10,

Keywords

cybersecurity, augmented reality, medical technology, artificial intelligence, and the Internet of Medical Things

Abstract

This review examines the progression of advanced medical technologies utilizing data sourced from Scopus and Web of Science. This study examines Artificial Intelligence (AI), the Internet of Medical Things (IoMT), Augmented Reality (AR), Big Data analytics, and cybersecurity in the context of medical devices. A systematic keyword-based search identified significant trends and contributions, indicating an increasing research interest in these domains. Analysis identified three primary research clusters: AI/AR, IoMT/cybersecurity, and embedded systems. The influence of AI on diagnostics, personalized treatment, and device usability is emphasized, as well as the role of AR in surgical procedures and medical education. The role of IoMT in continuous patient monitoring is examined, highlighting the essential requirement for strong cybersecurity protocols to safeguard sensitive patient information. The research identifies challenges such as the digital divide, interoperability issues, and the necessity for standardized protocols. Future directions involve utilizing Industry 4.0 technologies, such as cyber-physical systems, IoT, and cloud computing, to enhance smart manufacturing, develop personalized medical devices, and improve access to healthcare. The findings emphasize the transformative potential of these technologies and the necessity of addressing current challenges to achieve equitable and effective healthcare delivery.

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1. Alkatheiri MS: Artificial intelligence assisted improved human-computer interactions for computer systems. Comput. Electr. Eng. Jul. 2022; 101: 107950. 2. Wang N, Rebolledo-Mendez G, Matsuda N, et al.: Artificial Intelligence in Education. Cham: Springer Nature Switzerland; 2023; vol. 13916. 3. Manogaran G, Lopez D, Thota C, et al.: Big Data Analytics in Healthcare Internet of Things.2017; pp. 263–284. 4. Bhardwaj N, Wodajo B, Spano A, et al.: The Impact of Big Data on Chronic Disease Management. Health Care Manag (Frederick). Jan. 2018; 37(1): 90–98. 5. Luo P, Li ZS: A Review of Internet of Things (IoT) based Engineering Applications and Data Fusion Challenges for Multi-rate Multi-sensor Systems 2020 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE; Jun. 2020; pp. 1–7. 6. Gallos P, et al.: MedSecurance Project: Advanced Security-for-Safety Assurance for Medical Device IoT (IoMT).2023. 7. Dulhare UN, Kumar AVS, Dutta A, et al.: Handbook of Research on Artificial Intelligence and Soft Computing Techniques in Personalized Healthcare Services. New York: Apple Academic Press; 2024. 8. Arora S: IoMT (Internet of Medical Things): Reducing Cost While Improving Patient Care. IEEE Pulse. Sep. 2020; 11(5): 24–27. 9. Pradyumna GR, Hegde RB, Bommegowda KB, et al.: Empowering Healthcare With IoMT: Evolution, Machine Learning Integration, Security, and Interoperability Challenges. IEEE Access. 2024; 12: 20603–20623. 10. Abouelmehdi K, Beni-Hssane A, Khaloufi H, et al.: Big data security and privacy in healthcare: A Review. Procedia Comput. Sci. 2017; 113: 73–80. 11. Thomasian NM, Adashi EY: Cybersecurity in the Internet of Medical Things. Health Policy Technol. Sep. 2021; 10(3): 100549. 12. Messinis S, Temenos N, Protonotarios NE, et al.: Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review. Comput. Biol. Med. Mar. 2024; 170: 108036. 13. Pournik O, Mukherjee T, Ghalichi L, et al.: How Interoperability Challenges Are Addressed in Healthcare IoT Projects.2023. 14. Moawad GN, Elkhalil J, Klebanoff JS, et al.: Augmented Realities, Artificial Intelligence, and Machine Learning: Clinical Implications and How Technology Is Shaping the Future of Medicine. J. Clin. Med. Nov. 2020; 9(12): 3811. 15. van Eck NJ, Waltman L: Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. Aug. 2010; 84(2): 523–538. 16. Abdaoui A, Al-Ali A, Riahi A, et al.: Secure medical treatment with deep learning on embedded board. Energy Efficiency of Medical Devices and Healthcare Applications. Elsevier; 2020; pp. 131–151. 17. Holzinger A: Machine Learning for Health Informatics. vol. 9605. . Cham: Springer International Publishing; 2016. 18. Manickam P, et al.: Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors (Basel). Jul. 2022; 12(8): 562. 19. Rocha A, et al.: Edge AI for Internet of Medical Things: A literature review. Comput. Electr. Eng. May 2024; 116: 109202. 20. El-Saleh AA, Sheikh AM, Albreem MA, Honnurvali MS. The Internet of Medical Things (IoMT): opportunities and challenges. Wireless Networks. 2024 May 21:1-8. 21. Razdan S, Sharma S: Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. IETE Tech. Rev. Jul. 2022; 39(4): 775–788. 22. Shen J, Wang C, Lai CF, Wang A, Chao HC. Direction density-based secure routing protocol for healthcare data in incompletely predictable networks. IEEE Access. 2016 Dec 9;4:9163-73. 23. Ghubaish A, Salman T, Zolanvari M, et al.: Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security. IEEE Internet Things J. Jun. 2021; 8(11): 8707–8718. 24. Zulfiqar F, Raza R, Khan MO, et al.: Augmented Reality and its Applications in Education: A Systematic Survey. IEEE Access. 2023; 11: 143250–143271. 25. Pottle J: Virtual reality and the transformation of medical education. Future Healthc J. Oct. 2019; 6(3): 181–185. 26. Suresh D, Aydin A, James S, et al.: The Role of Augmented Reality in Surgical Training: A Systematic Review. Surg. Innov. Jun. 2023; 30(3): 366–382. 27. Tang KS, Cheng DL, Mi E, et al.: Augmented reality in medical education: a systematic review. Can. Med. Educ. J. Dec. 2019; 11: e81–e96. 28. Nagy A, Lagkas T, Sarigiannidis P, et al.: Evaluation of AI-Supported Input Methods in Augmented Reality Environment. 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE; Jun. 2023; pp. 496–503. 29. Ho D: The Piezoionic Effect: Biomimetic Transduction Mechanism for Sensing, Actuation, Interface, and Energy Harvesting. ChemElectroChem. Feb. 2024; 11(3). 30. Nguyen H-S, Voznak M: A Bibliometric Analysis of Technology in Digital Health: Exploring Health Metaverse and Visualizing Emerging Healthcare Management Trends. IEEE Access. 2024; 12: 23887–23913. 31. Bresch C, Hély D, Lysecky R, et al.: TrustFlow-X. ACM Trans. Embed. Comput. Syst. Sep. 2020; 19(5): 1–26. 32. Surrel G, Aminifar A, Rincon F, et al.: Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors. IEEE Trans. Biomed. Circuits Syst. Aug. 2018; 12(4): 762–773. 33. Simalatsar A, You W, Gotta V, et al.: Representation of Medical Guidelines with a Computer Interpretable Model. Int. J. Artif. Intell. Tools. Jun. 2014; 23(03): 1460003