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This is a Human Clinical Trial study (also classified as: Validation Study).

Artificial Intelligence Advances in Stroke Care: Addressing Clinical Implementation Challenges

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AI demonstrated high diagnostic performance for ischemic stroke using MRI, with a sensitivity of 93% and specificity rates.
Prognostic models reached an average Area Under the Curve of 0.872, while SVM and XGBoost showed improved prediction accuracy.
Comprehensive meta-synthesis of 33 studies provided insights on AI applications in diagnosis and rehabilitation of stroke.
Bridging the implementation gap in stroke care requires improved clinical trust and robust regulatory frameworks.

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Meta-synthesis reveals high AI diagnostic performance for stroke, indicating rehabilitation tools need improved validation.

Nagah et al. (2025) studied this question.

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