ReviewCancer reports (Hoboken, N.J.)2026
Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.
Review in Cancer reports (Hoboken, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundGastric cancer (GC) remains a major global health burden characterized by substantial heterogeneity in diagnosis, prognosis, and treatment response. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has been increasingly investigated for the analysis of endoscopic images, radiological scans, whole-slide pathology images, molecular profiles, liquid-biopsy data, and multimodal clinical datasets. This structured review summarizes recent AI-based developments in GC diagnosis, staging, prognostic assessment, and treatment-response prediction, with particular emphasis on the translational maturity of the available evidence. PubMed/MEDLINE, Web of Science, Embase, Scopus, and Google Scholar were searched for relevant English-language publications from database inception through January 2026. Eligible studies were evaluated according to clinical task, data modality, model type, cohort size, study design, validation strategy, interpretability, clinical workflow readiness, and reported limitations. RECENT
findingsRecent AI models have demonstrated promising performance in selected research settings, particularly in endoscopic image analysis, computational pathology, CT-based radiomics, and multimodal prognostic modeling. However, most studies remain retrospective, single-center, and internally validated. Calibration, robustness, prospective clinical utility, and generalizability across populations and institutions remain insufficiently assessed. Major barriers to translation include data heterogeneity, annotation variability, overfitting, data leakage, external validation failure, algorithmic bias, regulatory requirements, reimbursement, clinician-AI interaction, medicolegal responsibility, post-deployment monitoring, and model drift.
conclusionAI has considerable potential to support GC diagnosis, prognosis, and treatment planning, but most current models remain insufficiently validated for routine clinical use. Future progress will depend on transparent reporting, standardized validation, multicenter prospective trials, clinically meaningful endpoints, and the integration of explainable, trustworthy AI systems into routine clinical workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.