Preoperative Prediction of Difficult Laparoscopic Cholecystectomy: Evolution, Validation, and Emerging Machine-Learning Approaches-A Scoping Review

Background and Objectives: Preoperative prediction of difficult laparoscopic cholecystectomy (LC) has expanded from point-based scores to regression, nomogram, machine-learning, and quantitative imaging approaches. We examined whether this diversification was accompanied by stronger validation and clinical translation. Materials and Methods: Following a registered Open Science Framework protocol, we conducted a scoping review using Joanna Briggs Institute methodology and PRISMA-ScR. PubMed/MEDLINE, Scopus, and Web of Science were searched on 8 August 2026 for English-language publications from 1 January 2010 through 31 July 2026. Eligible studies evaluated structured multivariable tools using exclusively preoperative inputs. Screening and data charting were performed independently and in duplicate.