ReviewDigital health
Artificial intelligence in stroke management: From prevention to treatment.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this review, we aim to discuss the rising role of artificial intelligence (AI) across all phases of stroke care from prehospital triage, acute management, rehabilitation, to postdischarge care, and secondary prevention. AI has shown a remarkable potential in improving stroke patients' care through prehospital triage systems, refining risk stratification and personalizing rehabilitation protocols. The use of automated notification systems in prehospital triage has reduced door-to-neurointerventional times by approximately 40 min, speeding thrombectomy access and providing stroke patients time critical treatment that hugely affects their outcomes. In the hyperacute setting, deep learning helps to provide a more accurate estimation of lesion(s) on brain scans than standard conventional quantitative markers, even offering a faster processing time. In rehabilitation, the use of kinematic data in real time led to increasing utilization of robotics and virtual reality (VR) to aid motor recovery poststroke, and machine learning to map out complex nonlinear recovery paths for speech and cognition. However, the evidence linking AI integration to measurable improvements in patients outcomes is yet to be fully evaluated. Moving forward, these models need to be validated through multicenter prospective trials and standardized benchmarks. There is also a need to focus on developing systems that are designed for the user, ensuring they support rather than replace clinical expertise. AI's ultimate value will depend on rigorous validation, integration into clinical workflows, and sustained collaboration between developers and clinicians.
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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.