Abstract Background Chest pain remains one of the most common and challenging presentations in cardiovascular medicine. Clinical evaluation—including structured history-taking, recognition of anginal equivalents, and focused physical examination—continues to anchor early risk estimation. Content Artificial intelligence (AI) may augment cardiovascular specialist care through refined pre-test risk stratification by integrating clinical information, high-sensitivity troponin, ECG data, and multimodal imaging. Deep-learning algorithms applied to ECGs identify subtle ischemic patterns and support high–negative-predictive-value rule-out strategies. AI-enhanced coronary computed tomography (CT) and cardiac magnetic resonance (CMR) expand diagnostic capability by characterizing plaque, perfusion, and alternative non-ischemic etiologies. Multimodal models leveraging electronic health records produce dynamic risk estimates, while AI tools increasingly support identification of non-coronary but clinically relevant causes of chest pain. Summary The clinical value of AI will ultimately depend on rigorous validation, thoughtful implementation, and clinician governance. When appropriately integrated, AI has the potential to improve consistency, equity, and accuracy in the assessment of chest pain.
Imola et al. (2026) studied this question.