Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption. With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs. These advantages have resulted, at the moment, in more than 900 AI-based devices being approved, today, by regulatory entities, for clinical use, with similar to improved performance and efficiency compared to traditional technologies. However, issues such as model generalization, bias, transparency, interpretability, accountability, and data privacy remain significant barriers for broad adoption of these technologies. AI shows great promise in enhancing CVD care through more accurate and efficient approaches. Yet, widespread adoption is hindered by unresolved concerns of interested stakeholders. Addressing these challenges is crucial for fully integrating AI into clinical practice and shaping the future of CVD prevention, diagnosis and treatment.