Table 5. Applications of AI in COVID-19 psychological effects and data security.

First author [year] (reference)	Country (region)	Modality	Model	Data source	Application area	Result
Choi et al. [2020] (74)	USA	Sociodemographic questionnaire	ANN	Korean immigrants above the age of 18 residing in the U.S. were invited to respond to a survey by e-mails and posting on Korean immigrants’ online communities from 24 May 2020 to 14 June 2020	Psychological effects	AUC: 0.806
Wang et al. [2020] (75)	China	Sociodemographic questionnaire	XGBoost	3,800 non-graduating college students from a top multidisciplinary and research-oriented university directly under the jurisdiction of the Ministry of Education in North China were invited to attend the studies during February 15 to March 17, 2020	Psychological effects	Accuracy of Model 1: 79.26%; accuracy of Model 2: 84.38%
Jha et al. [2020] (76)	USA	Sociodemographic questionnaire	PGM	17,764 adults in the USA at different age groups, genders, and socioeconomic statuses	Psychological effects	Accuracy of high risk of depression group: 0.80; accuracy of low risk of depression group: 0.64
Kang et al. [2021] (77)	Korea	Pathological image data	PAIP	3,100 images acquired by the Department of Pathology at Seoul National University Hospital, Seoul National University Bundang Hospital, and SMG-SNU Boramae Medical Center	Data security	Accuracy of liver cancer: 83%; accuracy of prostate cancer: 86%; accuracy of kidney cancer: 80%

AI, artificial intelligence; COVID-19, coronavirus disease 2019; ANN, artificial neural network; XGBoost, extreme gradient boosting machine; PGM, Bayesian probabilistic graphical model; PAIP, pathology artificial intelligence platform.