Advances and Applications of Artificial Intelligence in Comprehensive Stroke Care

Artificial intelligence (AI) has emerged as a powerful enabler of innovation in stroke care, with growing applications across diagnosis, treatment, and recovery. Stroke remains a leading cause of death and long-term disability worldwide, underscoring the need for faster recognition, more accurate clinical decision-making, and personalized care strategies across all stages of management. Recent advances in machine learning and deep learning have accelerated progress in early stroke recognition, particularly in prehospital and telestroke settings, as well as in imaging interpretation and decision support during acute care. At the same time, AI-based approaches are increasingly being explored to support post-acute management, functional outcome prediction, and rehabilitation planning.Despite these advances, substantial challenges remain in translating AI-driven methods into routine clinical practice. These include ensuring robust validation across diverse populations, addressing ethical and equity considerations, integrating AI tools into real-world clinical workflows, and demonstrating meaningful improvements in patient outcomes. There is a clear need for systematic evaluation of how AI can be deployed across the full stroke care continuum, from initial recognition through long-term recovery.This Research Topic aims to critically examine the current state and emerging directions of artificial intelligence in comprehensive stroke care. The focus is on AI methodologies that support clinical decision-making and patient management across prehospital, acute, post-acute, and rehabilitation stages of care. Contributions should emphasize both methodological innovation and clinical relevance, with attention to translational impact, real-world deployment, and healthcare system integration.We welcome original research, reviews, and perspective articles that address AI applications spanning the stroke care continuum, from early detection and triage to recovery and rehabilitation, with an emphasis on evidence-based impact and clinical implementation. Relevant themes include, but are not limited to:- AI for early stroke recognition and triage, including prehospital assessment and telestroke workflows- Artificial intelligence in acute stroke care, including imaging interpretation, clinical decision support, and workflow optimization- AI-driven analysis of speech, language, physiological signals, eye movements, and motor function for neurological assessment- Post-acute and rehabilitation-focused AI approaches, including outcome prediction, personalized therapy planning, and recovery monitoring- Clinical validation, deployment, and ethical considerations for AI systems in stroke care across diverse populations and healthcare settings Topic editors: Mohamed Abul Hassan, Lara Zimmermann, Chad Aldridge, Yan Zhuang Source: https://www.frontiersin.org/research-topics/77237/advances-and-applications-of-artificial-intelligence-in-comprehensive-stroke-care