Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy

Abstract

Radical gastrectomy for gastric cancer demands meticulous pre-operative staging and real-time intra-operative guidance to optimise oncologic margins and minimize complications. Recent advances in artificial-intelligence algorithms reliably integrate multimodal clinical, imaging and pathological data, producing highly reproducible tumour-staging and risk-stratification models that inform personalised operative strategies. Concurrently, navigation platforms that fuse computed-tomography, magnetic-resonance, ultrasound and fluorescence datasets generate patient-specific three-dimensional reconstructions with sub-millimeter registration accuracy, enabling dynamic margin delineation and reducing inadvertent tissue injury. Predictive analytics that assimilate intra-operative metrics with early postoperative information can forecast survival and complication profiles, thereby supporting tailored follow-up protocols. Remaining barriers include safeguarding data privacy, accelerating image-registration and inference speeds, meeting high computational-resource demands and offsetting the substantial capital and maintenance costs of these systems. Nevertheless, the convergent evolution of artificial intelligence and real-time imaging navigation is poised to transform radical gastrectomy by elevating surgical precision, enhancing patient safety and improving long-term outcomes; realizing this promise will require algorithmic refinement, multicenter validation, robust ethical frameworks and cost-effective implementation models.

Keywords: Radical gastrectomy; Artificial intelligence; Real-time imaging navigation; Clinical translation; Precision medicine

Core Tip: Artificial-intelligence models now stage gastric tumours with near-radiologist accuracy, while multimodal three-dimensional navigation fuses computed tomography, magnetic resonance imaging, ultrasound and fluorescence data to guide sub-millimeter resections. Integrating these tools into radical gastrectomy reduces margin positivity and tissue trauma, and real-time analytics predict complications and survival, enabling personalised follow-up. We dissect the algorithms, navigation hardware and validation studies underpinning this leap, outline ethical and economic hurdles, and map a translational roadmap that could make data-driven, image-guided gastrectomy the new standard of care.