Abstract
BACKGROUND: The integration of AI in diagnosing and managing ACS shows increasing promise, yet challenges remain in translating AI-CDSS into clinical practice. This study evaluates the advancements and limitations of AI for ACS over the past three years, purpose of understanding the scope, limitations, and potential of AI-CDSS in ACS. MATERIALS AND METHODS: We conducted a systematic review of recent literature, adhering to guidelines for systematic reviews. We applied QUADAS-2 and PROBAST tools for quality assessment, focusing on biases in study designs. Ten studies about AI-CDSS in ACS management underwent critical analysis, emphasizing the strength of their research methods and the thoroughness of their prospective validation to ensure theoretical integrity and practical reliability. RESULTS: Our research reveals that while discourse around AI-CDSS in ACS management intensifies, obstacles hinder efficacy in practical settings. These challenges include biases in tests and unrepresentative patient selection, pointing to the need for rigorous and inclusive samples. The lack of sufficient external and prospective validation in studies also raises concerns clinical utility of AI-CDSS. The result is the gap between the potential benefits of AI-CDSS and the actual impact of improving diagnostic accuracy and outcomes for ACS limitations identified. CONCLUSIONS: While AI-CDSS shows promise for improving diagnostic accuracy, treatment efficacy, and workflows in ACS, this study highlights the imperative to enhance model validation, including prospective validation, and address lingering diagnostic gaps. Improving study design and mitigating biases remain crucial for the acceptance and effectiveness of AI-CDSS in acute cardiac care settings.