Artificial intelligence is revolutionizing cardiac surgery by optimizing patient outcomes and personalizing treatment strategies through advanced imaging analysis, predictive modelling, and real-time decision support. With rising cardiovascular disease rates and increasingly complex procedures, AI technologies offer unprecedented opportunities to minimize surgical risks, refine procedural planning, and improve access to expert-level cardiothoracic care worldwide.

This Methods Collection will provide a comprehensive resource for researchers, cardiologists, cardiothoracic surgeons, and computational scientists working at the intersection of AI and cardiothoracic surgery. It will showcase evidence-based methodologies, including preoperative planning algorithms, intraoperative decision algorithms, postoperative outcome prediction models, and surgical robotics integration to advance AI-driven innovations in cardiothoracic care. Featured content will include validated protocols for data acquisition and preprocessing, frameworks for machine learning model development and validation, clinical integration strategies, and ethical guidelines specific to AI deployment in cardiothoracic surgical settings.

Through this centralized repository, the research community will gain access to standardized approaches for developing and evaluating AI tools, promoting reproducible research and institutional collaboration. By bridging the gap between technological innovation and clinical implementation, this collection will ensure that AI advances translate into measurable improvements in cardiac surgical care and patient outcomes globally.