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.
Heeralal et al. (2025) studied this question.