Implementing Artificial Intelligence-Based COVID-19 Chatbot in the Kingdom of Bahrain

Authors

Keywords:

Artificial Intelligence, chatbot, Natural Language Processing, Machine Learning

Abstract

The sophistication in technology has provided a smart solution through the conversational AI-powered chatbots. Chatbots help the healthcare sector minimize operational costs and improve the quality and speed of healthcare services. Chatbots assisted the patients during the recent pandemic crisis by managing patients’ needs during the COVID-19. Due to the rapidly changing technology, these chatbots need to keep pace with the emerging technologies. Several chatbots were created to fight the pandemic. We studied these chatbots, identified their functions, strengths and weaknesses and proposed a state-of-the-art chatbot model based on machine learning. For the proposed chatbot, free and open-source tools and frameworks are used. Several criteria were applied to classify the chatbots. Further, these criteria were thoroughly defined and explained by the programming syntax. We also presented the performance analysis of the proposed chatbot. In this analysis, some characteristics of the chatbots were analyzed. These features include accuracy and reliability of the information, tailored assistance, ability to spread awareness, symptoms monitoring, mental health support, pressure on customer care executives, and some other key features. The chatbot can be updated and adapted according to future needs. We are confident that these features have made the proposed chatbot better than several other chatbots that exist in the market.

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Example of Bot Conversation-Vaccine Registration Intent.

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Published

30.09.2022

How to Cite

Natsheh, E. ., & Jabed, M. . (2022). Implementing Artificial Intelligence-Based COVID-19 Chatbot in the Kingdom of Bahrain. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 10–17. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2131

Issue

Vol. 10 No. 3 (2022)

Section

Research Article

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