Artificial intelligence in cardiology for non-experts: decoding key terms and understanding machine learning methodologies. A Scientific Statement of the ESC Working Group on e-Cardiology, the European Heart Rhythm Association (EHRA), the Association for Acute CardioVascular Care (ACVC), the Heart Failure Association (HFA), the European Association of Cardiovascular Imaging (EACVI), the European Association of Preventive Cardiology (EAPC), and the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC, the ESC Regulatory Affairs Committee, and the ESC Digital Cardiology and Artificial Intelligence Committee

Evidence-based medicine represents the systematic approach through which physicians today use the best available scientific evidence from clinical research to guide their clinical decision-making process. However, the emergence of artificial intelligence (AI), with its diverse models and potential applications in medicine, is leading to a new form of research driven by ‘big data’ and heterogeneous datasets, extending beyond conventional statistical analysis and thereby making AI literacy an increasingly essential competence for clinicians. Artificial intelligence applications in cardiology have shown potential to support disease diagnosis, risk prediction, workflow efficiency, and therapeutic decision-making. However, despite these opportunities, AI may also represent a technological barrier for many physicians, due to its mathematical foundations and the speed at which models evolve. A solid understanding of AI concepts is therefore necessary for cardiologists to critically interpret results and engage with this new research landscape. This scientific statement explores AI in cardiology from an educational perspective, outlining the key knowledge required to build adequate AI literacy. The goal is to enable cardiologists not only to avoid reliance on external experts for methodological decisions, but also to collaborate with greater insight and informed judgment.