Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction—a systematic review

Abstract

= 68%), which was explored through pre-specified subgroup analyses. Risk of bias was low in 18 of 32 diagnostic accuracy studies per QUADAS-2, while prognostic studies had a mean Newcastle-Ottawa Scale score of 6.8, with common limitations in confounder control and follow-up completeness. Multimodal models integrating imaging and clinical data generally outperformed unimodal approaches. This synthesis provides robust, quantitative evidence supporting the integration of AI into clinical workflows to enable precision stroke care, while also delineating critical challenges-including dataset bias, limited interpretability, and infrastructural disparities-that must be addressed to facilitate successful clinical implementation.

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