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The initial search was conducted up to December 6, 2024, and a supplementary search was conducted in April 2025 to identify newly published or newly indexed studies. Diagnostic performance metrics, including sensitivity, specificity, area under the curve (AUC), and 2\u2009\u00d7\u20092 diagnostic data, were extracted or reconstructed. A bivariate random-effects model was used for meta-analysis, and study quality was assessed using a modified QUADAS-2 tool with PROBAST-informed signaling questions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 18 studies were included: 13 studies evaluated AI-based models for pediatric asthma diagnosis and five studies evaluated AI-based models for predicting acute asthma exacerbations. For asthma diagnosis, internal validation showed a sensitivity of 0.87, specificity of 0.93, and AUC of 0.96, while external validation showed a sensitivity of 0.81 and specificity of 0.94. For acute exacerbation prediction, internal validation showed a sensitivity of 0.59, specificity of 0.79, and AUC of 0.68. These estimates should be interpreted cautiously because heterogeneity was very high and external validation evidence was limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-based models showed promising but preliminary diagnostic performance for pediatric asthma, whereas their performance for predicting acute exacerbations remained limited. The findings should be interpreted cautiously because of substantial heterogeneity, potential publication bias, and limited external validation. Future studies should use standardized definitions and independent external validation before these models are implemented in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03599-7","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:29:53Z","timestamp":1780068593000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"26","author":[{"given":"Qingxia","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"3599_CR1","unstructured":"Lizzo JM, Goldin J, Cortes S. 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