Artificial intelligence at the gut-oral microbiota frontier: mapping machine learning tools for gastric cancer risk prediction.

ABSTRACT:

Background

Gastric cancer (GC) remains a significant global health burden, with high mortality due to delayed diagnosis. Advances in microbiome profiling and artificial intelligence (AI) have opened new frontiers in non-invasive cancer risk prediction. However, the methodological landscape of AI-driven microbiome-based GC prediction remains fragmented and poorly standardized.

Objective

To systematically review and critically evaluate artificial intelligence (AI) and machine learning (ML) models developed for gastric cancer prediction using microbial and non-invasive biomarkers, spanning gut, gastric mucosal, and oral ecosystems as well as tongue-based imaging proxies. We aimed to map methodological rigor, translational readiness, and biomarker convergence across these domain

SUBMITTER: Azhdarimoghaddam A 

PROVIDER: S-EPMC12751584 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature