Artificial intelligence at the surgeon–pathologist interface: revolutionizing diagnosis, prognostication, and precision surgery

Abstract Artificial intelligence applications in surgical pathology encompass tumour detection, histological classification, grading, biomarker assessment, lymph node evaluation, margin analysis, and outcome prediction. Deep learning algorithms have demonstrated high diagnostic performance across multiple malignancies including breast, colorectal, prostate, lung, and gastrointestinal cancers. Emerging computational pathology platforms can predict molecular alterations directly from routine haematoxylin and eosin-stained slides, facilitating personalized treatment strategies while reducing dependence on expensive ancillary testing. AI-assisted pathology has also improved workflow efficiency, reduced interobserver variability, and enhanced diagnostic reproducibility. Importantly, AI-derived pathological insights are increasingly influencing surgical decision-making through improved risk stratification, optimized extent of resection, tailored lymphadenectomy, and prediction of treatment response.