ABSTRACT
Antimicrobial resistance (AMR) is a concern to public health, prompting the development of novel strategies for combating AMR. While the use of machine learning (ML) to AMR is in its infancy, it has made significant progress as a diagnosis tool, owing to the growing availability of phenotypic/genotypic datasets and much faster computational power. While applying ML in AMR research is viable, its use is limited. It has been used to predict antimicrobial susceptibility genotypes/phenotypes, discover novel antibiotics, and improve diagnosis when combined with spectroscopic and microscopy methods. ML implementation in healthcare settings has challenges to adoption due to concerns about model interpretability and data integrity. The focus of this review is to outline the significant benefits and drawbacks along with the salient trends reported in recent studies. © 2022 IEEE.
ABSTRACT
Agent-based modeling has been widely used in the simulation of global pandemics, which provides useful policy implications and helps contain the pandemic's spread. Through agent-based modeling (ABM), people gain insight into the transmission of the pandemic and develop better policies to contain its spread. This article introduces the existing agent-based models used in the pandemic, such as smallpox, H1N1, and COVID-19, and the conclusions about pandemic forecasting that the scientists have reached through ABM. The introduction also shows the development and improvement of ABM as the computational power increases. It has been concluded from the existing research that implementing contact tracing and lockdown regulations could contribute to the achievement of digital herd immunity and contain the spread of the pandemic. Currently, scientists are dedicated to making a more scalable version of the agent-based model to analyze the transmission of the virus on a global scale. © 2022 IEEE.
ABSTRACT
Artificial Intelligence, abbreviated as AI, is described as a collection of technologies that work together to allow machines to discern, interpret, take action, and acquire knowledge with human level of intelligence. The AI domain includes technologies such as natural language processing (NLP) and machine learning (ML). Each of these is on its separate route of development and, when combined with datasets, analytics, and computerization, may assist organisations in achieving their objectives, whether it's enhancing customer service or improving supply chain. The pipeline of the development of novel drugs is quite extensive and costly apart from being complicated. On average, this entire process requires around 12 years costing approximately 2.6 billion USD. In this age of computational power, AI-based techniques are used in almost all of the steps during the process of discovering and developing drugs. AI has been able to reduce the time requirement and amount of money involved in this process thus expediting the entire procedure of new pharmaceutical inventions. In this manuscript, we have discussed some of the major applications of AI in the pharmaceutical sector. Tools that are employed to forecast the toxicity of drugs and how some of them, for example - Toxtree, ADMET, ProTox etc. have been used by researchers in the drug discovery process, have also been talked about. Further we have mentioned how AI is proving to be a powerful tool in the fight against the COVID-19 pandemic, for example, in the detection of the SARS-Co V -2 virus, development of vaccines, genomics etc. In addition to this, we have discussed about the manner in which the penetration of AI-based companies into the pharmaceutical field has resulted in some notable outcomes, for instance - prediction of the 3-dimensional structure of proteins by DeepMind's AlphaFold2, determination of new drug contender for kidney fibrosis by Insilico Medicine's Chemistry42 etc. Furthermore, we have stated the propitious future that AI is expected to bring about in the pharma world with a special focus on drug development. © 2022 IEEE.
ABSTRACT
In order to effectively prevent the spread of COVID19, people from different parts of the world were supposed to be wearing face masks after the WHO put it as a primordial instruction to stop its propagation. Researchers from different backgrounds gathered their efforts to ensure the respect of wearing face mask, namely AI field researchers. In this research, we are interested on the AI applications that were done from the beginning of the pandemic to prevent the COVID 19 contamination, especially those related to the mask wearing detection. The detection of wearing mask is classified as a computer vision problem, more specifically, an object detection one. Besides, with the evolution of the computational power and the availability of huge number of datasets, deep learning models using image and video processing techniques were proposed in order to detect people transgressing the wearing mask rule. In this paper we introduce a literature review of object detection, a case study of this problem which consists in the wearing mask detection, the related works as well as the different proposed solutions, and the suggested general pipeline for the treatment of this problem. © 2021 IEEE.
ABSTRACT
In 5G Release 17 specific work items are dealing with medical applications. Moreover, the COVID-19 pandemic has accelerated the adoption of mobile-health (m-health) and e-health. This paper proposes the implementation of a m-health framework supporting social distancing management. Experimental results show that by exploiting 5G connectivity and the computational power provided by an accelerated edge cloud, the proposed framework can perform social distancing verification faster than a user equipment (UE)-based deployment. © 2021 IEEE.