โก Quick Summary
This narrative review highlights the transformative role of artificial intelligence (AI) in stroke care, focusing on its applications in diagnostic, predictive, and workflow domains. The findings suggest that AI tools can significantly enhance clinical decision-making and operational efficiency in acute stroke management.
๐ Key Details
- ๐ Dataset: 300 records reviewed, 46 studies met criteria
- ๐งฉ Focus: Ischemic and hemorrhagic stroke outcomes
- โ๏ธ Technology: AI platforms like RapidAI and Viz.ai
- ๐ Validation: Some tools validated in multicenter studies
๐ Key Takeaways
- ๐ง AI enhances diagnostic imaging by detecting large vessel occlusions and hemorrhages.
- ๐ Predictive modeling tools assist in forecasting outcomes and stratifying hemorrhagic risks.
- ๐ Workflow applications improve communication and reduce treatment delays.
- โ๏ธ Ethical concerns include dataset bias and access disparities.
- ๐ก Future research is needed for multicenter validation and cost-effectiveness studies.
- ๐ AI tools are designed to augment, not replace, clinical judgment.
- ๐ International studies included only if applicable to U.S. practice.
- ๐ฎ AI’s evolution reflects a shift towards infrastructural augmentation in stroke care.
๐ Background
Stroke remains a leading cause of morbidity and mortality worldwide, necessitating rapid and accurate interventions. The integration of artificial intelligence into stroke care presents an opportunity to enhance diagnostic accuracy, predict patient outcomes, and streamline workflows. This review synthesizes the current landscape of AI applications in stroke management, providing insights into their effectiveness and areas for improvement.
๐๏ธ Study
The review analyzed peer-reviewed literature published between 2015 and 2024, focusing on studies that reported clinical, operational, or system-level outcomes related to ischemic or hemorrhagic strokes. A structured search across multiple databases identified relevant studies, emphasizing the importance of real-world applications and external validation of AI tools in clinical settings.
๐ Results
AI platforms have shown promising results in diagnostic imaging, particularly in the rapid detection of critical conditions such as large vessel occlusions and hemorrhages. Predictive modeling tools have been effective in stratifying risks and forecasting patient outcomes. However, while some tools like RapidAI and Viz.ai have undergone multicenter validation, many others remain in early development stages, highlighting the need for further research and validation.
๐ Impact and Implications
The integration of AI in stroke care has the potential to revolutionize how clinicians approach diagnosis and treatment. By enhancing diagnostic capabilities and improving workflow efficiency, AI tools can lead to faster and more accurate interventions, ultimately improving patient outcomes. However, addressing ethical concerns and ensuring equitable access to these technologies will be crucial for their successful implementation in diverse healthcare settings.
๐ฎ Conclusion
This review underscores the significant potential of artificial intelligence in transforming stroke care. As AI technologies evolve from proof-of-concept to integral components of clinical practice, ongoing research and ethical considerations will be essential to maximize their benefits. The future of stroke management looks promising with AI as a supportive tool that enhances clinical decision-making and operational efficiency.
๐ฌ Your comments
What are your thoughts on the integration of AI in stroke care? We would love to hear your insights! ๐ฌ Join the conversation in the comments below or connect with us on social media:
Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications.
Abstract
Artificial intelligence (AI) has emerged as a transformative force in stroke care, with increasing integration into diagnostic, predictive, and operational domains. This narrative review synthesizes the applications of AI in acute stroke management, drawing on peer-reviewed literature published between 2015 and 2024. A structured search of PubMed, Google Scholar, Semantic Scholar, National Center for Biotechnology Information (NCBI), and Litmaps identified 300 records, of which 46 met predefined criteria. Eligible studies were peer-reviewed, in English, and focused on ischemic or hemorrhagic stroke with reported clinical, operational, or system-level outcomes; studies limited to algorithm development or non-original data were excluded. “Real-world” contexts were defined as those involving implemented or externally validated tools, while international studies were included only when their findings were directly applicable to U.S. practice. This review was conducted narratively, organized by diagnostic, predictive, and workflow domains. In diagnostic imaging, AI platforms have demonstrated efficacy in detecting large vessel occlusions, hemorrhage, and perfusion deficits, expediting triage in time-critical scenarios. Predictive modeling tools support outcome forecasting and hemorrhagic risk stratification, while workflow applications such as AI-powered coordination platforms improve communication, accelerate decision-making, and reduce treatment delays. Some tools, including RapidAI and Viz.ai, have undergone multicenter validation, but most remain in early or proof-of-concept phases. Ethical concerns persist, particularly regarding dataset bias, lack of interpretability, and uneven access to advanced imaging infrastructure. Cost-effectiveness analyses remain sparse, leaving uncertainty about scalability in resource-limited settings. Collectively, these tools function not as autonomous decision-makers but as augmentative supports that reinforce clinical judgment and operational efficiency. The current evidence base highlights gaps that future research must address: multicenter prospective validation, standardized cost-effectiveness studies, equity-focused deployment, and explainability frameworks. Despite these limitations, AI is increasingly positioned as a scaffolding mechanism within stroke systems, enhancing rather than replacing the work of clinicians. Its evolution reflects a shift from proof-of-concept innovation to infrastructural augmentation, with its future impact contingent on rigorous validation, ethical design, and system-level alignment.
Author: [‘Heeralal VT’, ‘Chadee SE’, ‘Ilyaev B’, ‘Ilyaev R’, ‘Ilyayeva S’]
Journal: Cureus
Citation: Heeralal VT, et al. Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications. Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications. 2025; 17:e93430. doi: 10.7759/cureus.93430