SynthesisBMC medical informatics and decision making2026
Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.
Synthesis in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Artificial intelligence (AI) holds significant promise for transforming cerebral infarction care, yet its real-world performance across the entire disease management continuum remains inadequately synthesized, with heterogeneous evidence and unclear clinical translation pathways. To address this gap, this systematic review and meta-analysis comprehensively evaluates the performance and application value of AI models in diagnosis, subtype classification, severity grading, treatment guidance, and prognosis prediction for cerebral infarction. Adhering to PRISMA 2020 guidelines, we identified 76 eligible studies (n = 45,289 patients) published between 2015 and 2025. Pooled estimates were derived using bivariate random-effects meta-analysis for diagnostic accuracy and random-effects models with restricted maximum-likelihood estimation for other outcomes. AI models demonstrated robust diagnostic accuracy (pooled sensitivity: 0.92, specificity: 0.89, AUC: 0.95), and consistently high performance in classification (accuracy: 0.87), grading (AUC: 0.91), treatment response prediction (AUC: 0.88), and 3-month functional outcome prediction (AUC: 0.90). Substantial heterogeneity was observed across diagnostic studies (I
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