Artificial intelligence in coronary plaque characterization and risk assessment: from images to impact

Accurate characterization of coronary atherosclerotic plaque and individualized cardiovascular risk assessment remain active challenges in clinical and interventional cardiology. In recent times, Artificial Intelligence (AI) has emerged as a powerful new tool able to support clinicians in determining diagnoses and prognoses from coronary imaging. This commentary focuses on the current applications of AI in coronary plaque imaging, particularly on coronary computed tomography angiography (CCTA), intravascular ultrasound (IVUS), and optical coherence tomography (OCT), evaluating its role in identifying high-risk plaque features and predicting future adverse cardiovascular events. We discuss limitations of conventional assessment methods, illustrating how AI algorithms can improve reproducibility, reduce operator dependence, and examine current evidence from registries and clinical studies. Furthermore, some key challenges remain to be addressed, including data quality, model generalizability, clinical integration, and regulatory concerns. We argue that AI’s promise lies not in replacing clinical expertise, but in empowering coronary risk stratification and characterization. Ongoing validation and clinician-AI collaboration will be essential to ensure meaningful patient outcomes.