Key result
AI models accurately diagnose ACS and outperform traditional risk scores for predicting adverse cardiac events.
Why the study?
Rapid and accurate diagnosis of ACS is critical for timely intervention, and AI has emerged as a tool to improve diagnostic accuracy and risk stratification, but challenges and ethical issues remain.
This narrative review outlines the current landscape, methodologies, and challenges of implementing AI for diagnosis and risk stratification in acute coronary syndrome.
Answers are grounded in this paper and cite their sources.
May aid ACS diagnosis and risk stratification; leaves open the need for prospective validation before clinical adoption.
Acute coronary syndrome (ACS) is a global health concern that requires rapid and accurate diagnosis for timely intervention and better patient outcomes. With the emergence of Artificial Intelligence (AI), significant advancements have been made in improving diagnostic accuracy, efficiency, and risk stratification in ACS management. This narrative review examines the current landscape of AI applications in ACS diagnosis and risk stratification, emphasizing key methodologies, technical and clinical implementation challenges, and also possible future research directions. Moreover, unlike previous reviews, this paper also focuses on ethical and legal issues and the feasibility of clinical applications.
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Mariani et al. (2025) conducted a review in Acute Coronary Syndrome. Artificial Intelligence vs. Standard clinical assessment and traditional risk scores was evaluated. Artificial intelligence models demonstrate high accuracy in diagnosing acute coronary syndrome and often outperform traditional risk scores in predicting long-term mortality and adverse cardiac events.
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