Artificial intelligence in cardiovascular risk assessment: Opportunities, challenges, and future directions

Artificial intelligence (AI) and machine learning (ML) are increasingly influencing cardiovascular medicine, offering new opportunities for risk stratification, disease detection, and personalized patient management.In their review, Artificial Intelligence and Machine Learning in Cardiovascular Risk Assessment: A Practical Clinical Framework, Novaes et al. (1) provide a timely and clinically oriented overview of contemporary AI methodologies and their potential applications in cardiovascular risk assessment.The article successfully introduces complex computational concepts in a manner accessible to clinicians while emphasizing practical considerations for implementation in routine cardiovascular care.A major strength of the review is its structured discussion of supervised learning algorithms, ensemble methods, deep learning techniques, and emerging generative AI applications.The authors appropriately highlight the importance of data quality, external validation, model calibration, interpretability, and ethical considerations.These issues remain critical because successful clinical adoption of AI depends not only on predictive accuracy but also on transparency, reproducibility, and clinician confidence in model outputs.