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Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning

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  • Mohammad (Behdad) Jamshidi

    (Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 30614 Pilsen, Czech Republic)

  • Omid Moztarzadeh

    (Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic
    Department of Anatomy, Faculty of Medicine in Pilsen, Charles University, 30100 Pilsen, Czech Republic)

  • Alireza Jamshidi

    (Dentistry School, Babol University of Medical Sciences, Babol 47176-47745, Iran)

  • Ahmed Abdelgawad

    (College of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USA)

  • Ayman S. El-Baz

    (Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA)

  • Lukas Hauer

    (Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic)

Abstract

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine’s performance.

Suggested Citation

  • Mohammad (Behdad) Jamshidi & Omid Moztarzadeh & Alireza Jamshidi & Ahmed Abdelgawad & Ayman S. El-Baz & Lukas Hauer, 2023. "Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning," Future Internet, MDPI, vol. 15(4), pages 1-15, April.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:142-:d:1118134
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    References listed on IDEAS

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    1. Zhenming Jin & Xiaoyu Du & Yechun Xu & Yongqiang Deng & Meiqin Liu & Yao Zhao & Bing Zhang & Xiaofeng Li & Leike Zhang & Chao Peng & Yinkai Duan & Jing Yu & Lin Wang & Kailin Yang & Fengjiang Liu & Re, 2020. "Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors," Nature, Nature, vol. 582(7811), pages 289-293, June.
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    Cited by:

    1. Mohammad (Behdad) Jamshidi & Arash Dehghaniyan Serej & Alireza Jamshidi & Omid Moztarzadeh, 2023. "The Meta-Metaverse: Ideation and Future Directions," Future Internet, MDPI, vol. 15(8), pages 1-31, July.

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