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The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Findings<\/jats:title>\n                <jats:p>Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P\u2009&gt;\u20090.05).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Interpretation<\/jats:title>\n                <jats:p>Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02256-7","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T09:02:32Z","timestamp":1691571752000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"23","author":[{"given":"Yu","family":"Xin","sequence":"first","affiliation":[]},{"given":"Hongxu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Mu","sequence":"additional","affiliation":[]},{"given":"Han","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Zipeng","family":"Zhuo","sequence":"additional","affiliation":[]},{"given":"Hongyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongying","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xutong","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Changsong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Kaijiang","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"2256_CR1","unstructured":"WHO. 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