Abstract
Machine learning (ML) is increasingly applied to predict operative time in surgery, yet its role in robotic ventral hernia repair (VHR) remains unexplored. Brucchi and colleagues recently developed ML models, Random Forest, Gradient Boosting, and Ridge Regression, to predict operative time in 208 patients undergoing elective robotic VHR, achieving modest performance with Random Forest (R² = 0.22, MAE = 38 min). While this proof‑of‑concept study represents a novel application, several methodological considerations warrant attention before clinical translation. These include the absence of comparison with traditional statistical methods, the omission of surgeon‑level temporal data and learning curve dynamics, the lack of external validation, and the gap between SHAP‑based interpretability and actionable clinical utility. This Matters Arising offers a perspective on these considerations and proposes directions for future refinement, including incorporation of surgeon‑specific metrics, benchmarking against conventional regression models, external validation, and integration into clinical workflows, to enhance the predictive accuracy and clinical applicability of ML‑based operative time prediction in abdominal wall surgery.