SynthesisVascular health and risk management2025
Artificial Intelligence in Cerebrovascular Disease Management: A Comprehensive Review of Risk Prediction, Diagnosis, Therapeutic Optimization, and Clinical Translation.
Synthesis in Vascular health and risk management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebrovascular diseases (CVDs) impose a heavy global health burden, necessitating efficient management strategies. Artificial intelligence (AI) has become a key transformative tool across the CVD care continuum, and this review systematically synthesizes AI's latest advancements, limitations, and clinical translation pathways in CVD management, adhering to PRISMA-ScR guidelines. A literature search was performed in four core databases (PubMed, Web of Science, EMBASE, IEEE Xplore) for studies published between 2018-2023. After strict screening (inclusion: original research/clinical trials with clear indicators; exclusion: unvalidated studies/conference abstracts), 128 high-quality studies were included, with quality assessed via NOS and QUADAS-2. Key AI applications in CVD management include: (1) Risk prediction: Multimodal models (radiomics-CFD, EHR-imaging) achieve AUC >0.9, but performance declines in elderly patients (>75 years, ΔAUC=0.08-0.12); (2) Diagnosis: Systems like Viz LVO and DeepHemorrhage reduce LVO detection time to 6 minutes and hemorrhage segmentation Dice to 0.94, yet face false positives (3.5-5%) and workflow delays; (3) Therapeutic optimization: Intraoperative AI (eg, Siemens AI-Path) shortens microcatheter placement time by 61%, and pharmacogenomic models cut antiplatelet complications by 37%; (4) Long-term monitoring: Mobile platforms (eg, NeuroVision™) automate NIHSS scoring (ICC=0.93) but lose accuracy in home settings (ICC=0.85-0.88). Critical limitations of current AI include single-center data bias, poor interpretability, and legal risks (unclear misdiagnosis liability). This review proposes three innovative solutions: a "data-model-clinical" closed loop, a multidimensional AI value evaluation system, and defining the "human-AI collaboration boundary" in neurointerventions. Future directions focus on primary care-adapted lightweight models, comorbidity-specific algorithms, and AI-assisted rehabilitation. This review emphasizes that physician-AI collaboration and standardized frameworks (eg, AI-RADS, WHO-ITU guidelines) are critical for AI's sustainable translation in CVD care. Addressing current gaps will enable AI to further improve therapeutic efficiency and functional outcomes, alleviating the global CVD burden.
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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.