###### 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. artificial intelligence; big data; deep learning; drug discovery; edge computing; internet of things; internet of medical things; natural language processing +
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enabling healthcare providers and public health officials to make informed decisions. Furthermore, IoT devices can allow remote patient monitoring, reducing exposure risks and minimizing the strain on healthcare systems'. Overall, the integration of the IoT into healthcare and public health systems offers an innovative solution to the challenges posed by the pandemic, such as lockdown situations, providing an automated and transparent treatment process. This is especially important during these difficult times [7]. The importance and time-sensitive nature of this matter have driven the development of various new technologies. One such technology is edge computing (EC), which involves distributing computational resources closer to consumers to increase computation speed and enhance bandwidth utilization [8; 9; 10]. This technology involves performing the majority of computations on edge devices (EDs) located in close proximity to the areas in which Internet of Things (IoT) devices require data processing. Edge devices are robust resources that can improve pandemic situations by enabling the use of intelligent medical equipment (IME) and other support equipment. Various tools, such as webcams, drones, intelligent gadgets, sensors, and robots, are beneficial in pandemic situations. Therefore, a framework that combines deep learning (DL) and EC can significantly improve the efficiency and productivity of conventional methods [11; 12]. In particular, an EC platform is proposed to expedite drug development during clinical phases. By contrast, existing medical methods for clinical trials do not use real-time technology to monitor vaccinated individuals. Our approach allows the symptoms and healthcare information of vaccine candidates to be monitored and managed through smartphones [12]. This paper is organized into six sections. In Section 1, we introduce the topic of EC and its potential applications in healthcare. Section 2 provides a background on EC and IoT technology. Section 3 explores state-of-the-art IoT applications in medicine. Section 4 delves into the intersection of EC, DL, the Internet of Medical Things (IoMT), and drug discovery.