The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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This study is concerned with the development of a deep neural network-based framework, including a “convolutional neural network (CNN)” encoder and a “Long Short-Term Memory (LSTM)” decoder in an automatic image captioning application. The proposed model percepts information points in a picture and their relationship to one another in the viewpoint. Firstly, a CNN encoder excels at retaining spatial information and recognizing objects in images by extracting features to produce vocabulary that describes the photos. Secondly, an LSTM network decoder is used for predicting words and creating meaningful sentences from the built keywords. Thus, in the proposed neural network-based system, the VGG-19 model is presented for defining the proposed model as an image feature extractor and sequence processor, and then the LSTM model provides a fixed-length output vector as a final prediction. A variety of images from several open-source datasets, such as Flickr 8k, Flickr 30k, and MS COCO, were explored and used for training as well as testing the proposed model. The experiment was done on Python with Keras and TensorFlow backend. It demonstrated the automatic image captioning and evaluated the performance of the proposed model using the BLEU (BiLingual Evaluation Understudy) metric.

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