Translating Artificial Intelligence into Precision Gastric Cancer Care

Abstract

Artificial intelligence (AI) is being investigated across the gastric cancer care pathway, but the evidence required for clinical adoption extends beyond predictive accuracy. This narrative review critically exam ines AI applications in endoscopy, computed tomography, digital pathology, multimodal modeling, tumor microenvironment assessment, molecular profiling, and surgical assistance. Selected studies demonstrate substantial progress in lesion detection, staging, response prediction, and prognostic stratification. However, retrospective discrimination, prospective validation, and improved outcomes under AI-guided care represent distinct evidentiary achievements. A large randomized endoscopy trial illustrates this distinction: AI reduced examination blind spots but did not significantly improve the primary endpoint of gastric neoplasm detection after pathology review. Elsewhere, externally validated imaging and pathology models remain vulnerable to differences in acquisition protocols, patient selection, treatment context, and data availability. Associations between model scores and treatment outcomes cannot establish that a score identifies differential treatment benefit. Multimodal and molecular approaches introduce additional challenges related to missing inputs, as say reproducibility, and implementation costs. We propose a staged translational framework linking a clearly defined clinical decision to independent validation, prospective workflow evaluation, comparative impact assessment, and monitored deployment. Priorities include clinically appropriate reference standards, calibration and threshold-specific performance, subgroup evaluation, transparent reporting, human factors testing, and economic assessment. Clinical adoption should depend on reproducible incremental value within a specified care pathway, with safeguards proportionate to the consequences of error. These requirements provide a practical basis for converting promising gastric cancer AI models into reliable clinical tools.