ReviewNature reviews. Neurology2020
Artificial intelligence for decision support in acute stroke - current roles and potential.
Review in Nature reviews. Neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 4 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
44 citing papers in PubMed, 4 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment.Journal of neurology · 2023Pooled it
- Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.World neurosurgery · 2022Pooled it
- Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trial.BMJ (Clinical research ed.) · 2026Trial
- Deep Learning-Based Automated Clinical Gait Assessment From Kinematic Data in People With Stroke: Development and Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.Journal of the American Heart Association · 2026Article
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- Roadmapping stroke healthcare in Colombia: a comprehensive telestroke network evaluation.BMC medical informatics and decision making · 2026Article
- Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.Journal of medical systems · 2026Article
- Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.Saudi medical journal · 2026Review
- Effectiveness of artificial intelligence as a support tool in preoperative anaesthesia assessments for cataract surgery: a step towards enhancing patient management.British journal of anaesthesia · 2025Article
- Large Language Models in Neurology Treatment Decision-Making: a Scoping Review.Journal of medical systems · 2025Article
- Emergency medical services providers' perspectives on the use of artificial intelligence in prehospital identification of stroke- a qualitative study in Norway and Sweden.BMC emergency medicine · 2025Article
- Healthcare professionals' knowledge, attitudes, and practices towards predictive diagnosis of early neurological deterioration.Scientific reports · 2025Article
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- Validation of a Machine-Learning Clinical Decision Aid for the Differential Diagnosis of Transient Loss of Consciousness.Neurology. Clinical practice · 2025Article
- Intravenous tissue plasminogen activator in functional stroke mimics.Postepy psychiatrii neurologii · 2025Review
- Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study.Journal of medical Internet research · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification and treatment of patients with stroke is becoming increasingly complex as more treatment options become available and new relationships between disease features and treatment response are continually discovered. Consequently, clinicians must constantly learn new skills (such as clinical evaluations or image interpretation), stay up to date with the literature and incorporate advances into everyday practice. The use of artificial intelligence (AI) to support clinical decision making could reduce inter-rater variation in routine clinical practice and facilitate the extraction of vital information that could improve identification of patients with stroke, prediction of treatment responses and patient outcomes. Such support systems would be ideal for centres that deal with few patients with stroke or for regional hubs, and could assist informed discussions with the patients and their families. Moreover, the use of AI for image processing and interpretation in stroke could provide any clinician with an imaging assessment equivalent to that of an expert. However, any AI-based decision support system should allow for expert clinician interaction to enable identification of errors (for example, in automated image processing). In this Review, we discuss the increasing importance of imaging in stroke management before exploring the potential and pitfalls of AI-assisted treatment decision support in acute stroke.
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Registered trials
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.