The Ambulatory Surgery Center Paradox
William M. Zhao, Benjamin J. Schwartz, Gabriel A. BratBackground:
Ambulatory surgery centers now perform more than 60% of the 60 million elective surgical procedures in the United States, yet artificial intelligence research in surgery has focused almost exclusively on hospital-based implementations. This evidence gap has implications for technology validation, investment decisions, and patient safety as ASCs adopt hospital-validated systems without setting-specific evidence.
Methods:
We conducted a scoping literature review searching PubMed, Scopus, Web of Science, and Cochrane Library from January 2020 through September 2025 for studies explicitly examining AI or machine learning applications in ASCs with quantitative outcomes. A parallel search using hospital-related terminology enabled direct comparison of research volume.
Results:
After screening 847 potentially relevant articles, fewer than 10 studies specifically examined AI in ASC settings, while more than 500 hospital-based studies were identified during the same period. Existing ASC research focused on predictive analytics for workflow optimization; no studies examined intraoperative AI applications. The limited amount of ASC-specific research makes it unclear if hospital-validated benefits translate across settings to ASCs.
Conclusions:
A profound evidence gap exists between ASC surgical volume and ASC-focused AI research. The operational characteristics that distinguish ASCs from hospitals position them as ideal research settings for rigorous AI validation. Systematic ASC research is urgently needed to inform implementation decisions and establish data-standardization, technical, and economic frameworks required for the setting where most surgical patients receive care.