Large language surgery in the era of AI and digital standards

Abstract

Surgeons are increasingly encountering artificial intelligence (AI) across the research to practice continuum. In surgical settings, this includes computer vision applied to imaging and operative video, predictive modeling for perioperative risk and outcomes, and large language models that generate and transform clinical data. The potential gains are substantial, but translation has so far outpaced methodological consistency, reporting quality, and readiness for safe adoption. These gaps matter particularly in surgery, where performance is both operator and environment-dependent, and where digital and visual data are central to decision making and intervention delivery. This review maps where AI can integrate along the surgical pathway and uses the translational continuum as a framework, from problem specification and model development through clinical evaluation, reporting, and implementation. We describe how the expanding standard ecosystem can strengthen surgical AI evidence by improving transparency, reproducibility, and interpretability, and by supporting more consistent critical appraisal. We also outline why generative systems warrant additional scrutiny, given output variability, hallucinations, and rapid update cycles. A lifecycle, standards-led approach offers a practical route to more effective and safer clinical adoption.

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