Artificial Intelligence in Stroke Detection Using CT Imaging in Indian Hospitals: A Systematic Review (P2-5.004)

Andrea Buendia; Prashasti Upadhyay; Victoria Loosigian; Anindita Deb

doi:10.1212/wnl.0000000000217507

June 9, 2026Neurology

Artificial Intelligence in Stroke Detection Using CT Imaging in Indian Hospitals: A Systematic Review (P2-5.004)

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Key Points

This systematic review aims to assess the impact of artificial intelligence on stroke detection using CT imaging in Indian hospitals.
Conducted a systematic review of existing studies on AI applications in stroke detection using CT imaging.
Analyzed metrics of diagnostic accuracy, including sensitivity and specificity of AI algorithms.
Evaluated the clinical implications of integrating AI in hospital settings across India.
AI-enhanced stroke detection showed improved accuracy rates compared to traditional methods.
Sensitivity rates for AI systems ranged from 80% to 95%, significantly higher than manual interpretations.
Integration of AI could reduce diagnosis time, suggesting enhanced patient outcomes.

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Implication

Systematic review investigates AI's effectiveness in detecting strokes through CT imaging in Indian hospitals, indicating potential improvements in diagnosis.

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Buendia et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3d6faef96ed7f0563d0 https://doi.org/10.1212/wnl.0000000000217507

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Artificial Intelligence in Stroke Detection Using CT Imaging in Indian Hospitals: A Systematic Review (P2-5.004)

Key Points

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Discussion

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Artificial Intelligence in Stroke Detection Using CT Imaging in Indian Hospitals: A Systematic Review (P2-5.004)

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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