A leap into the future: excluding the ischaemic origin of chest pain through artificial intelligence

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. Artificial intelligence (AI) may augment cardiovascular specialist care through refined pre-test risk stratification by integrating clinical information, high-sensitivity troponin, electrocardiogram (ECG) data, and multimodal imaging. Deep-learning algorithms applied to ECGs identify subtle ischaemic patterns and support rule-out strategies with a high negative predictive value. Artificial intelligence-enhanced coronary computed tomography and cardiac magnetic resonance expand diagnostic capability by characterizing plaque, perfusion, and alternative non-ischaemic aetiologies. 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. 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.