@Article{info:doi/10.2196/43112,
author="Lim, Wendell Adrian
and Custodio, Razel
and Sunga, Monica
and Amoranto, Abegail Jayne
and Sarmiento, Raymond Francis",
title="General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Jan",
day="5",
volume="26",
pages="e43112",
keywords="COVID-19; health chatbot; conversational agent in health care; artificial intelligence; systematic review; mobile phone",
abstract="Background: A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews. Objective: This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework. Methods: We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines. Results: Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user. Conclusions: The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response. ",
issn="1438-8871",
doi="10.2196/43112",
url="https://www.jmir.org/2024/1/e43112",
url="https://doi.org/10.2196/43112",
url="http://www.ncbi.nlm.nih.gov/pubmed/38064638"
}