Artificial Intelligence in Non-invasive Hemodynamic Monitoring: A Systematic Review of Accuracy, Effectiveness, and...

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.