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.

Opioid Use Disorder is a chronic condition characterized by compulsive opioid use despite negative consequences, resulting in severe health risks such as overdose and contraction of infectious diseases. High dropout rates in opioid agonist therapy highlight the need for more effective relapse prevention strategies. Animal and clinical studies indicate that opioids influence gut microbiota, which in turn plays a critical role in addiction development and alters behavioral responses to opioids. This study provides a comprehensive review of the literature on the effects of opioids on the gut microbiome and explores the potential of microbiome manipulation as a therapeutic target in opioid addiction.

The development of novel biomarkers to predict postoperative complications (POCs) after colorectal cancer (CRC) surgery is essential, both for improving treatment outcomes and for facilitating the widespread use of same-day discharge following colectomy. This longitudinal observational study, a sub-study of a previous RCT, investigated the prognostic potential of a panel of novel biomarkers, including inflammatory cytokines (TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-17 A, INF-γ, IP-10, MCP-1, TGF-β1), alongside conventional biomarkers such as CRP and WBC levels, measured preoperatively and on postoperative day 6, for predicting POCs following CRC resection. Among 40 recruited patients, 38 were included in the final analysis. Patients who did not experience POCs had a significantly lower preoperative level of TNF-α (2.28 pg/mL vs. 68.77 pg/mL, p = 0.022). ROC curve analysis showed that TNF-α has significant (p = 0.012) discriminative capacity to distinguish between patients with POCs and those with an uneventful postoperative course, with an AUC of 0.722 (95% CI: 0.555, 0.889). Multivariable analysis suggested that a low preoperative TNF-α level (< 55.35 pg/mL) may be an independent predictor of an uneventful postoperative course (Odds Ratio = 0.137, 95% CI: 0.023, 0.836). This study showed that preoperative levels of TNF-α demonstrates a moderate discriminative capacity to distinguish between patients with POCs and those with an uneventful postoperative course in patients undergoing surgery for left-side CRC, although the findings are limited by the small sample size and should be validated in larger cohorts. Clinical Trial Registration: NCT04013841, date of registration 2019-07-10.

Background/Objectives: Early and accurate detection of postoperative complications (POCs) remains a major challenge in colorectal cancer (CRC) surgery, underscoring the need for reliable molecular biomarkers. This study evaluated whether plasma miR-21 and miR-181a can predict POCs following left-sided CRC resection. Methods: This longitudinal observational sub-study was conducted within a randomized controlled trial. Adult patients undergoing elective left-sided CRC resection were included. Plasma miR-21 and miR-181a levels were measured preoperatively and on postoperative day (POD) 6 using RT-qPCR. POCs were assessed according to Clavien–Dindo classification. Of 40 enrolled patients, 38 were included in the final analysis (15 with and 23 without postoperative complications). Discriminative performance was assessed using receiver operating characteristic analysis and correlations with inflammatory markers were evaluated. Results: No significant differences in plasma miR-21 or miR-181a levels were observed between groups at baseline or POD6 (all p > 0.05). Both biomarkers showed limited discriminative ability (AUC = 0.61 and 0.54, respectively), while a combined model of miR-181a + TNF-α improved performance (AUC = 0.76, 95% CI: 0.57, 0.94, p = 0.01). At baseline, miR-21 correlated strongly with miR-181a (ρ = 0.81, p < 0.001) and moderately inversely with TNF-α (ρ = −0.35, p = 0.043). Conclusions: MiR-21 or miR-181a measured at baseline or POD6 show limited predictive value for POCs after CRC surgery. Further studies would benefit from larger sample sizes and optimized sampling strategies that reflect possible early dynamic changes in these biomarkers.

This prospective, cross-sectional study evaluated reaction time (RT) variations across different sensory stimuli to investigate the efficacy of haptic feedback (HF) in reducing response latency for telesurgical applications. Three healthy-volunteer age cohorts (18–25, 35–45, and 55–65 years) were tested using visual, auditory, superficial, and deep sensations, alongside a multimodal stimulus combining visual and superficial inputs to simulate HF. The findings revealed that combined visual and superficial stimulation yielded a mean RT of 227 ± 27 ms, outperforming visual-only stimulation by 40 ms (95% CI: 32–48 ms) and superficial-only stimulation by 26 ms (95% CI: 20–33 ms) (p = 0.001). While this performance boost was consistent across all age groups, the 55–65 age cohort demonstrated the most pronounced reduction in RT when the combined stimuli were used. These results suggest that integrating tactile sensations with visual cues significantly mitigates latency compared to unimodal inputs, underscoring the potential of haptic feedback to enhance operator performance and safety in latency-sensitive environments like remote surgery.