To summarize the current literature on the role of AI in managing stroke patients, emphasizing its critical applications in prevention, acute management, rehabilitation, and outcome prediction.
Key Findings:
AI technologies, particularly ML and DL, are increasingly used in stroke care, improving diagnostic accuracy and treatment efficiency, as evidenced by specific studies.
AI can identify high-risk patients for stroke through retinal imaging and predictive modeling, with notable AUROC values.
Machine learning frameworks enhance early recognition of stroke symptoms in prehospital settings, improving triage and care, as demonstrated in comparative studies.
Interpretation:
AI is transforming stroke care by enabling faster and more accurate diagnosis and treatment, thus shifting the management model from reactive to precision-based, ultimately improving patient outcomes.
Limitations:
The review does not conduct a systematic quality assessment of included studies, which may affect the reliability of findings.
The findings are based on literature published in English, potentially limiting the scope and applicability of results.
Conclusion:
AI holds significant promise in stroke care, but challenges remain in clinical implementation and validation of these technologies, which must be addressed to maximize their impact on patient outcomes.
