Abstract
Artificial intelligence (AI) is rapidly reshaping cardiovascular imaging, shifting the field from traditional image interpretation toward integrated, data-driven disease characterization. AI can support every step of the imaging pathway—from protocol selection and image acquisition to reconstruction, quantification, and predictive modeling. These capabilities are especially valuable in a landscape marked by rising imaging volumes, increasing healthcare costs, and workforce burnout. By automating repetitive tasks and improving consistency, AI can streamline workflows and enhance reproducibility.
Cardio-oncology is uniquely positioned to benefit from these advances, as modern cancer care depends on serial imaging to detect subclinical cardiotoxicity from chemotherapy and radiotherapy. AI-enabled tools may improve the early identification of subtle functional changes and can support non-cardiology clinicians by providing reliable cardiac function measurements directly at the point of care.
However, several challenges must be addressed before AI can be fully integrated into routine cardio-oncology practice. Current studies are limited by small, heterogeneous datasets, variations in imaging protocols, and differences across vendors, underscoring the need for large, multicenter validation. Ethical considerations—including transparency, data privacy, and equitable implementation—also remain critical.
In conclusion, AI has the potential to transform cardio-oncology from a reactive discipline into one centered on early detection, individualized risk prediction, and proactive prevention.