ReviewCureus2025
Artificial Intelligence in Non-invasive Hemodynamic Monitoring: A Systematic Review of Accuracy, Effectiveness, and Clinical Applicability in Cardiology.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Usability of a Patch-Type Ultrasound System for Non-Invasive Hemodynamic Monitoring: A Simulation Study in Anesthesiologists.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hemodynamic monitoring is essential in cardiology for guiding diagnosis and therapy, but conventional invasive methods carry procedural risks while non-invasive methods often lack accuracy. The integration of artificial intelligence (AI) into monitoring devices offers opportunities to improve predictive accuracy, diagnostic yield, and workflow efficiency. This systematic review evaluated the role of AI-enhanced non-invasive hemodynamic monitoring devices in cardiology, focusing on effectiveness, accuracy, and clinical applicability. A comprehensive search of PubMed, Scopus, Web of Science, and Cochrane CENTRAL from inception to November 2024 retrieved 4,856 records; after duplicate removal, 4,158 articles were screened, 23 full texts were assessed, and nine studies met the inclusion criteria. Across diverse populations and settings, AI models consistently outperformed conventional approaches in predicting circulatory failure and hypotension, with an area under the receiver operating characteristic curve exceeding 0.90 in several studies. Non-invasive diagnostic enhancements included AI-electrocardiography for coronary artery disease detection and automated coronary calcium scoring from computed tomography scans with near-perfect agreement to expert readers. Invasive imaging applications demonstrated faster and more accurate intravascular ultrasound analysis, while workflow-focused systems reduced alarm fatigue without compromising safety. Most studies were of good methodological quality, although limitations included retrospective designs, heterogeneous populations, and limited prospective validation. Overall, AI-enhanced non-invasive hemodynamic monitoring shows strong potential to shift cardiovascular care from reactive detection to predictive and proactive management by improving accuracy, efficiency, and usability across diagnostic and monitoring domains, but large-scale prospective trials are needed to confirm real-world clinical impact, ensure equitable adoption, and address challenges related to interpretability and integration.
Indexed as
Identifiers
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.