Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesia Across the Continuum of Perioperative Care

Introduction Artificial intelligence (AI) and machine learning (ML) are becoming increasingly powerful tools in every aspect of healthcare. These technologies have significantly transformed diagnostic and clinical management of patients across the continuum of care in cardiac anesthesiology, a subfield emphasizing perioperative management. With the ability to analyze large datasets at unprecedented rates, AI-powered risk models have been shown to improve preoperative evaluations by predicting complications, including mortality and cardiac risk. Intraoperatively, ML algorithms have been effective in optimizing hemodynamic monitoring and improving image analysis for procedures like transesophageal echocardiography. In terms of postoperative care, AI models aid anesthesiologists in creating personalized pain management regimens, as well as in managing acute risks after surgery. Methods This study is a narrative review encompassing expert opinions, results from randomized controlled trials, and observational studies relating to the applications of AI and ML in cardiac anesthesia. Results 68 pertinent studies were evaluated and synthesized to provide a contextualized approach to the role of AI and ML across the perioperative care continuum. Conclusions While challenges such as algorithm bias and clinician training currently remain, AI presents itself as a tool in cardiac anesthesia to create efficient, patient-centered solutions while maintaining the highest standards of safety and accountability. This review highlights the vast potential of artificial intelligence and machine learning across the perioperative continuum and calls on cardiac anesthesiologists to adapt to the evolving landscape of digital medicine.