[Artificial Intelligence in Acute Ischemic Stroke:Time-Oriented Diagnostic Support and Treatment Prediction]

Artificial intelligence (AI) and machine learning (ML) are increasingly used in acute ischemic stroke. Their clinical value depends not only on diagnostic accuracy but also on whether these systems improve time-critical decision-making. Current applications include prehospital triage, detection of large-vessel and medium-vessel occlusions, automated ASPECTS assessment, estimation of ischemic core and penumbra, collateral evaluation, and workflow notification using CT, CT angiography, CT perfusion, and MRI. AI is also being investigated to predict treatment response, futile recanalization, symptomatic intracranial hemorrhage, brain edema, and final infarct volume after reperfusion therapy. In clinical practice, prognostic prediction must be distinguished from treatment-effect prediction. Useful AI systems should report uncertainty and calibration and provide actionable outputs at the clinically relevant time point while remaining embedded in a human-in-the-loop workflow. Future multimodal platforms may integrate imaging, clinical data, transfer information, procedure-related variables, and device characteristics to support individualized acute stroke workflows and endovascular procedural planning.