ReviewJournal of clinical medicine2023
Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies.
Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled 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.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Emergent Large Vessel Occlusion Detection Using Viz.ai Software and Its Impact on Stroke Workflow Metrics and Patient Outcomes in Stroke Centers: A Systematic Review and Meta-analysis.Translational stroke research · 2025Pooled it
- Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.Journal of neurointerventional surgery · 2026Trial
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Review
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- Cerebral Infarction: Epidemiology, Classification, Mechanisms, Diagnosis, and Management.MedComm · 2026Review
- Advanced Drug Delivery Strategies for Overcoming Biological Barriers: Tumor Microenvironment and Blood-Brain Barrier.Drugs in R&D · 2026Review
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Artificial intelligence in rehabilitation: a review of clinical effectiveness, real-world performance, safety, and equity across modalities and settings.Frontiers in digital health · 2026Review
- Evolution of CT perfusion software in stroke imaging: from deconvolution to artificial intelligence.European radiology · 2026Review
- Review
- Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.Current neurology and neuroscience reports · 2025Review
- Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.Scientific reports · 2025Article
- From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.GeroScience · 2025Article
- Transforming diagnosis through artificial intelligence.NPJ digital medicine · 2025Article
- Article
- Global, regional, and national burden of ischemic stroke in older adults (≥60 years) from 1990 to 2021 and projections to 2030.Frontiers in neurology · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study.AJNR. American journal of neuroradiology · 2024Article
- Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.Neurosciences (Riyadh, Saudi Arabia) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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What OpenQuestion holds
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.