Artificial intelligence in stroke management: From prevention to treatment

In this review, we aim to discuss the rising role of artificial intelligence (AI) across all phases of stroke care from prehospital triage, acute management, rehabilitation, to postdischarge care, and secondary prevention. AI has shown a remarkable potential in improving stroke patients' care through prehospital triage systems, refining risk stratification and personalizing rehabilitation protocols. The use of automated notification systems in prehospital triage has reduced door-to-neurointerventional times by approximately 40 min, speeding thrombectomy access and providing stroke patients time critical treatment that hugely affects their outcomes. In the hyperacute setting, deep learning helps to provide a more accurate estimation of lesion(s) on brain scans than standard conventional quantitative markers, even offering a faster processing time. In rehabilitation, the use of kinematic data in real time led to increasing utilization of robotics and virtual reality (VR) to aid motor recovery poststroke, and machine learning to map out complex nonlinear recovery paths for speech and cognition. However, the evidence linking AI integration to measurable improvements in patients outcomes is yet to be fully evaluated. Moving forward, these models need to be validated through multicenter prospective trials and standardized benchmarks. There is also a need to focus on developing systems that are designed for the user, ensuring they support rather than replace clinical expertise. AI's ultimate value will depend on rigorous validation, integration into clinical workflows, and sustained collaboration between developers and clinicians.