Evidence map›Paper›PMID 40343612›Full record

ReviewCurrent neurology and neuroscience reports2025

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

Murad M Alqadi, Sarkis G Morales Vidal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current neurology and neuroscience reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Murad M AlqadiDepartment of Surgery, College of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA.
Sarkis G Morales VidalDepartment of Neurology, Loyola University Health System, Maywood, IL, 60153, USA. sarkis.moralesvidal@lumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewArtificial 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

findingsConvolutional, 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. 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.

Indexed as

Artificial IntelligenceClinical Decision-MakingNeuroimagingNeurologyStrokeHumansArtificial intelligence (AI)Convolutional Neural Networks (CNNs)Hybrid modelsLarge language models (LLMs)Recurrent Neural Networks (RNNs)Transformer models

Identifiers

PMID40343612

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.