aceAI models to predict operative difficulty in laparoscopic cholecystectomy
Source article: Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis
Abstract: Accurately predicting operative difficulty in laparoscopic cholecystectomy (LC) is foundational to personalized surgical planning and patient safety assurance. However, the reliability, generalizability, and true clinical utility of current Artificial Intelligence (AI) models are currently unsubstantiated. This review aimed to evaluate the predictive performance and methodological quality of AI models designed to predict LC surgical difficulty. PubMed, Embase, Web of Science, and the Cochrane Library were search…
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By August 13, 2026, a systematic review and meta-analysis of 18 studies found AI models predicted laparoscopic cholecystectomy difficulty with pooled AUCs of 0.848 in training and 0.818 in validation, with ensemble models reaching 0.889 and 0.861. The review searched four databases to March 2, 2026 and used PROBAST and GRADE to assess bias and certainty.
The findings matter because accurate difficulty prediction could support personalized surgical planning and patient safety, but the evidence base as of the review date was dominated by high-bias studies lacking external validation and calibration. Whether ensemble and multimodal approaches will translate into safe clinical use remains uncertain pending prospective multicenter testing.
- Systematic review and meta-analysis of 18 studies searched to March 2, 2026, registered PROSPERO CRD420251267805.
- Pooled AUC was 0.848 for 27 training models and 0.818 for 32 validation models using random-effects meta-analysis.
- Ensemble architecture achieved highest pooled AUCs of 0.889 training and 0.861 validation.
- Multimodal integration of clinical features, imaging, and intraoperative video yielded superior performance.
- Risk of bias assessed with Prediction Model Risk of Bias Assessment Tool; certainty evaluated with GRADE.
AI models predicted operative difficulty in laparoscopic cholecystectomy with pooled discrimination of 0.848 in training and 0.818 in validation, with ensemble and multimodal models performing best.
Most AI models for predicting laparoscopic cholecystectomy difficulty carry high risk of bias, rarely undergo external validation, and have significant methodological flaws limiting clinical translation.
The rundown
The review searched PubMed, Embase, Web of Science, and Cochrane Library from inception to March 2, 2026, following PRISMA guidelines. Eighteen studies were included, with 27 training models and 32 validation models pooled. Areas under the curve with 95% confidence intervals were pooled using random-effects meta-analysis.
Bias and applicability were assessed with the Prediction Model Risk of Bias Assessment Tool and overall certainty with GRADE. Sixteen of 18 studies were judged at high risk of bias. Authors noted multimodal integration and ensemble architecture as promising but concluded models require strict evaluation and prospective multicenter testing before implementation.
Most included studies had high risk of bias, lacked external validation and proper calibration, and showed significant methodological flaws that limit clinical translation pending prospective multicenter testing.
Sources
- Peer-reviewedSurgical Endoscopy2026-08-13
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