Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning
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 progres...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/4/142 |
| _version_ | 1827745037824294912 |
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| author | Mohammad (Behdad) Jamshidi Omid Moztarzadeh Alireza Jamshidi Ahmed Abdelgawad Ayman S. El-Baz Lukas Hauer |
| author_facet | Mohammad (Behdad) Jamshidi Omid Moztarzadeh Alireza Jamshidi Ahmed Abdelgawad Ayman S. El-Baz Lukas Hauer |
| author_sort | Mohammad (Behdad) Jamshidi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:00:29Z |
| format | Article |
| id | doaj.art-cbfd1482cbee4f3e8496a8e2412506d5 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T05:00:29Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-cbfd1482cbee4f3e8496a8e2412506d52023-11-17T19:20:14ZengMDPI AGFuture Internet1999-59032023-04-0115414210.3390/fi15040142Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep LearningMohammad (Behdad) Jamshidi0Omid Moztarzadeh1Alireza Jamshidi2Ahmed Abdelgawad3Ayman S. El-Baz4Lukas Hauer5Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 30614 Pilsen, Czech RepublicDepartment of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech RepublicDentistry School, Babol University of Medical Sciences, Babol 47176-47745, IranCollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USADepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USADepartment of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech RepublicThe 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.https://www.mdpi.com/1999-5903/15/4/142artificial intelligencebig datadeep learningdrug discoveryedge computinginternet of things |
| spellingShingle | Mohammad (Behdad) Jamshidi Omid Moztarzadeh Alireza Jamshidi Ahmed Abdelgawad Ayman S. El-Baz Lukas Hauer Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning Future Internet artificial intelligence big data deep learning drug discovery edge computing internet of things |
| title | Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning |
| title_full | Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning |
| title_fullStr | Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning |
| title_full_unstemmed | Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning |
| title_short | Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning |
| title_sort | future of drug discovery the synergy of edge computing internet of medical things and deep learning |
| topic | artificial intelligence big data deep learning drug discovery edge computing internet of things |
| url | https://www.mdpi.com/1999-5903/15/4/142 |
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