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

Authors :
Mohammad (Behdad) Jamshidi
Omid Moztarzadeh
Alireza Jamshidi
Ahmed Abdelgawad
Ayman S. El-Baz
Lukas Hauer
Source :
Future Internet; Volume 15; Issue 4; Pages: 142
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

ISSN :
19995903
Volume :
15
Database :
OpenAIRE
Journal :
Future Internet
Accession number :
edsair.doi.dedup.....43f18c2391ea2305245f74cf8d4fc7c2
Full Text :
https://doi.org/10.3390/fi15040142