Table 3. Applications of AI in COVID-19 progression.

First author [year] (reference)	Country (region)	Modality	Model	Data source	Sample size	Result
Li et al. [2020] (51)	China	CT image	U-Net	COVID-19 patients in Shanghai Jiao Tong University Affiliated Sixth People’s Hospital from February 10, 2020 to April 9, 2020	COVID-19 cases classified as non-severe group on admission: 123	(CT-SS) AUC 0.66; accuracy 62.6%; sensitivity 58.97%; specificity: 64.29% (GGO volume cm3) AUC 0.639; accuracy 43.9%; sensitivity 79.49%; specificity: 45.24% (GGO volume percentage): AUC 0.694; accuracy 62.6%; sensitivity 64.1%; specificity: 69.05%; (consolidation volume cm3): AUC 0.796; accuracy 78.05%; sensitivity 71.79%; specificity: 80.95%; (consolidation volume percentage): AUC 0.79; accuracy 78.86%; sensitivity 79.49%; specificity: 78.57%
Yang et al. [2020] (52)	China	CT image	CT-SS	COVID-19 patients in Chongqing Three Gorges Central Hospital from January 21, 2020 to February 5, 2020	COVID-19 cases: 102	AUC: 0.892; sensitivity: 83.3%; specificity: 94%

AI, artificial intelligence; COVID-19, coronavirus disease 2019; CT-SS, CT severity score; AUC, area under the curve; GGO, ground-glass opacity.