Potential impact of artificial intelligence in predicting cerebrovascular events in patients with carotid artery stenosis

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Systematic review highlights AI's potential in predicting cerebrovascular events, suggesting improved patient outcomes.

AI prediction tools outperformed traditional regression models, indicating significant improvements in forecasting outcomes.
Discrimination area under the receiver operating characteristic curve (AUROC) varied, ranging from 0.71 to 0.99 across studies.
Most studies had high risk of bias, suggesting caution in the application of AI models without external validation.
Novel predictive tools have identified unfamiliar risk factors, indicating a need for further research in patient prognostics.

Coelho et al. (2025) studied this question.

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View Full Paper

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Authors

Systematic review highlights AI's potential in predicting cerebrovascular events, suggesting improved patient outcomes.

AI prediction tools outperformed traditional regression models, indicating significant improvements in forecasting outcomes.
Discrimination area under the receiver operating characteristic curve (AUROC) varied, ranging from 0.71 to 0.99 across studies.
Most studies had high risk of bias, suggesting caution in the application of AI models without external validation.
Novel predictive tools have identified unfamiliar risk factors, indicating a need for further research in patient prognostics.

Coelho et al. (2025) studied this question.

View Full Paper

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