Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains underrecognized across emergency and cardiology settings. Its heterogeneous presentation and overlapping mechanisms (vasospasm, plaque erosion/rupture, stent thrombosis) challenge timely diagnosis and tailored therapy.This review synthesizes contemporary evidence on pathophysiology, clinical spectrum, and management of Kounis syndrome (Types I-III), and map how artificial intelligence (AI) could enhance care pathways. We outline data streams relevant to AI (ECG, biomarkers, imaging, clinical narratives), modeling approaches (machine learning, sequence models), and decision-support scenarios (early detection, risk stratification, treatment selection, and monitoring). We also address implementation barriers, including dataset shift, bias, transparency, and governance.Kounis syndrome is a prime candidate for AI-enabled diagnostic support given its pattern-based signatures at the interface of allergy and acute coronary syndromes. Near-term priorities include curated multicenter datasets with harmonized phenotyping, prospective validation of models embedded in clinical workflows, and hybrid rules-plus-learning systems aligned with guideline-based care. Integrating allergy severity, hemodynamics, and ischemic burden into dynamic risk tools may reduce missed diagnoses and optimize anti-allergic and antithrombotic strategies while safeguarding patient safety.