Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions

Ambulatory surgery enhances resource utilization through reduced hospital stays and costs without compromising clinical outcomes. However, existing workflows are labor-intensive and repetitive, necessitating optimization in patient selection, assessment, admission notifications, no-show management, patient education, and postoperative follow-up. Artificial intelligence (AI) offers promising solutions to these challenges. This narrative review aimed to outline current AI applications in ambulatory surgery, appraise limitations, and discuss actionable pathways for future innovation. The PubMed database was systematically searched. Inclusion criteria were original research on AI in ambulatory surgery. Exclusion criteria covered weak thematic connections and unavailable full texts. Two researchers independently conducted the search and data extraction. 50 articles were analyzed in this review. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly used for preoperative patient selection and no-show prediction, intraoperative patient information verification, real-time monitoring and decision support, and postoperative recovery monitoring and health guidance. Nonetheless, AI implementation faces challenges such as data heterogeneity, algorithm interpretability, ethical concerns, and regulatory hurdles. AI demonstrates significant potential to optimize ambulatory surgery procedures, enhance clinical decision-making, and improve patient outcomes. Standardized data collection, collaborative data-sharing, transparency, and model validation with clinically meaningful endpoints are essential for robust and extensive AI application in ambulatory surgery. These elements can ultimately enhance the efficiency and safety of ambulatory surgical procedures.