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

Area of Science:

• Cardiology
• Allergy
• Artificial Intelligence

Background:

• Kounis syndrome, acute coronary events from hypersensitivity reactions, is underrecognized.
• Its varied presentations and mechanisms complicate diagnosis and treatment.

Purpose of the Study:

• To review Kounis syndrome pathophysiology, clinical spectrum, and management.
• To explore artificial intelligence (AI) applications for enhancing Kounis syndrome care pathways.

Main Methods:

• Synthesized contemporary evidence on Kounis syndrome.
• Outlined AI data streams (ECG, biomarkers, imaging, narratives) and modeling approaches.
• Discussed AI decision-support scenarios and implementation barriers.

Main Results:

• Kounis syndrome is suitable for AI-driven diagnostic support due to its pattern signatures.
• AI can aid in early detection, risk stratification, and treatment selection.

Conclusions:

• AI integration requires curated datasets, validation, and addressing bias.
• Dynamic AI tools incorporating allergy, hemodynamics, and ischemia can optimize care and reduce missed diagnoses.