Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.

Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.

Background: Surgery for rectal cancer has evolved, with the adoption of minimally invasive and robotic techniques. This study evaluates whether robotic surgery for rectal cancer produces an improvement in perioperative and oncological outcomes compared with open and laparoscopic surgery in a retrospective analysis. Methods: A single-center retrospective study included 212 patients with histologically confirmed rectal cancer treated between 2012 and 2021. The patients were grouped by surgical approach: robotic (RR, n = 62), laparoscopic (LR, n = 68), and open resection (OR, n = 82). The primary endpoints were total mesorectal excision (TME) quality, operative time, and hospital stay. The secondary endpoints included the lymph node yield, the conversion rate, and the 5-year survival outcomes (OS and DFS), analyzed via Kaplan–Meier curves and proportional hazards models. Results: The TME quality was high across the groups (RR: 91.9%, LR: 86.8%, OR: 95.1%). The median operative time was the longest in the RR group (304 min vs. 221–222 min in LR/OR). Robotic surgery resulted in shorter median hospital stays (10 (RR) vs. 14 (LR) vs. 14 (OR) days) and a lower conversion rate (3.2% vs. 14.7% in LR). The lymph node yield was highest in the LR group (27.9), followed by the RR (25.5) and OR (23.0) groups. Postoperative pneumonia was most common in the OR group (12.2%), and bladder dysfunction occurred only in the OR group (4.9%). The five-year OS and DFS did not differ notably between the groups. Conclusions: Robotic surgery offers advantages in short-term outcomes, including fewer complications, shorter hospitalization, and lower conversion rates, despite longer operative times. Its oncological efficacy is equal compared to those of laparoscopic and open surgery.

This international multicenter cohort study included 30 centers. Patients with duodenal adenocarcinoma (DAC), intestinal-type (AmpIT) and pancreatobiliary-type (AmpPB) ampullary adenocarcinoma, distal cholangiocarcinoma (dCCA), and pancreatic ductal adenocarcinoma (PDAC) were included. The primary outcome was 30-day or in-hospital mortality, and secondary outcomes were major morbidity (Clavien-Dindo 3b≥), clinically relevant post-operative pancreatic fistula (CR-POPF), and length of hospital stay (LOS). Results: Overall, 3622 patients were included in the study (370 DAC, 811 AmpIT, 895 AmpPB, 1083 dCCA, and 463 PDAC). Mortality rates were comparable between DAC, AmpIT, AmpPB, and dCCA (ranging from 3.7% to 5.9%), while lower for PDAC (1.5%, p = 0.013). Major morbidity rate was the lowest in PDAC (4.4%) and the highest for DAC (19.9%, p < 0.001). The highest rates of CR-POPF were observed in DAC (27.3%), AmpIT (25.5%), and dCCA (27.6%), which were significantly higher compared to AmpPB (18.5%, p = 0.001) and PDAC (8.3%, p < 0.001). The shortest LOS was found in PDAC (11 d vs. 14–15 d, p < 0.001). Discussion: In conclusion, this study shows significant variations in perioperative mortality, post-operative complications, and hospital stay among different periampullary cancers, and between the ampullary subtypes. Further research should assess the biological characteristics and tissue reactions associated with each type of periampullary cancer, including subtypes, in order to improve patient management and personalized treatment.