Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) enhances acute stroke care by improving diagnostics, prediction, and workflow efficiency. While promising, AI tools augment clinical judgment, requiring further validation and ethical considerations for widespread adoption.
Area Of Science
- Neurology
- Medical Informatics
- Biomedical Engineering
Background
- Artificial intelligence (AI) is increasingly integrated into healthcare, offering potential advancements in stroke management.
- Current stroke care pathways face challenges in timely diagnosis, prediction, and operational efficiency.
Purpose Of The Study
- To review and synthesize the applications of AI in acute stroke management from 2015 to 2024.
- To evaluate the efficacy, limitations, and future directions of AI in stroke diagnostics, prediction, and workflow.
Main Methods
- A narrative review of peer-reviewed literature from major databases (PubMed, Google Scholar, etc.) published between 2015 and 2024.
- Inclusion criteria focused on AI applications in ischemic or hemorrhagic stroke with clinical, operational, or system-level outcomes, excluding purely algorithmic studies.
- Studies were categorized into diagnostic, predictive, and workflow domains, with emphasis on real-world applications and U.S. practice relevance.
Main Results
- AI demonstrates efficacy in diagnostic imaging for detecting critical stroke indicators like large vessel occlusions and hemorrhage, speeding up triage.
- Predictive AI tools aid in forecasting patient outcomes and stratifying hemorrhagic risks.
- Workflow applications, including AI-powered coordination, improve communication and reduce treatment delays, though many tools are in early development stages.
Conclusions
- AI tools serve as augmentative supports for clinicians in stroke care, enhancing efficiency rather than replacing human judgment.
- Further research is crucial for multicenter prospective validation, cost-effectiveness studies, equitable deployment, and explainability frameworks.
- The future of AI in stroke care depends on rigorous validation, ethical design, and seamless integration into existing healthcare systems.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
