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TRV-2026-0787Certified recordPeer-reviewed

Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis

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…

Health · The Trace — both readings · certified 2026-08-16 · v1 · article view · machine-readable

Current reading — gain

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.

Current reading — problem

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.

What this doesn’t fix

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.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-0787, v1: “Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis.” Truvace, 2026-08-16. /record/TRV-2026-0787 (accessed at citation time). sha256 a33d80416fb1cec2…

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