AI-Guided Decision Support in Acute Cardiac Care: From Chest Pain to STEMI

Abstract

Artificial intelligence (AI) is rapidly transforming the landscape of acute cardiac care, offering novel opportunities to enhance diagnostic accuracy, risk stratification, and clinical decision-making. This literature review explores the current and emerging applications of AI in managing acute cardiovascular conditions, including myocardial infarction, arrhythmias, and heart failure. Methods such as machine learning, deep learning, and natural language processing have demonstrated potential in analyzing electrocardiograms, imaging, electronic health records, and wearable data to support timely and individualized care. Despite encouraging results from retrospective studies and pilot implementations, several barriers hinder broader clinical integration. Key limitations include data quality issues, lack of model transparency, clinician skepticism, regulatory uncertainties, and concerns about equity and bias. The review emphasizes the need for prospective validation, interpretability, workflow integration, and interdisciplinary collaboration to ensure safe and effective deployment. Future directions include the development of multimodal and foundation models, AI-enabled point-of-care tools, and frameworks for bias mitigation and regulatory oversight. As AI technologies evolve, their sustainable and ethical implementation will be essential to achieving meaningful improvements in patient outcomes and health system efficiency.

Ähnliche Arbeiten