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Purpose of Review <p>Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome prediction to clarify value drivers and execution risks.</p> Recent Findings <p>Convolutional, recurrent, and transformer architectures now trigger large‑vessel‑occlusion alerts, delineate ischemic core in seconds, and forecast 90‑day function. Commercial deployments—RapidAI, Viz.ai, Aidoc—report double‑digit reductions in door‑to‑needle metrics and expanded thrombectomy eligibility. However, dataset bias, opaque reasoning, and limited external validation constrain scalability. Hybrid image‑plus‑clinical models elevate predictive accuracy but intensify data‑governance demands.</p> Summary <p>AI can operationalize precision stroke care, but enterprise‑grade adoption requires federated data pipelines, explainable‑AI dashboards, and fit‑for‑purpose regulation. Prospective multicenter trials and continuous lifecycle surveillance are mandatory to convert algorithmic promise into reproducible, equitable patient benefit.</p>

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Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models

  • 期刊论文

  • 发表日期：

    2025年5月9日

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  • Murad M. Alqadi,
  • Sarkis G. Morales Vidal

摘要

Purpose of Review

Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome prediction to clarify value drivers and execution risks.

Recent Findings

Convolutional, recurrent, and transformer architectures now trigger large‑vessel‑occlusion alerts, delineate ischemic core in seconds, and forecast 90‑day function. Commercial deployments—RapidAI, Viz.ai, Aidoc—report double‑digit reductions in door‑to‑needle metrics and expanded thrombectomy eligibility. However, dataset bias, opaque reasoning, and limited external validation constrain scalability. Hybrid image‑plus‑clinical models elevate predictive accuracy but intensify data‑governance demands.

Summary

AI can operationalize precision stroke care, but enterprise‑grade adoption requires federated data pipelines, explainable‑AI dashboards, and fit‑for‑purpose regulation. Prospective multicenter trials and continuous lifecycle surveillance are mandatory to convert algorithmic promise into reproducible, equitable patient benefit.