Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.

Gastric cancer (GC) remains a significant global health challenge with high mortality rates, often due to late-stage diagnosis. We hypothesize that Raman spectroscopy (RS) (a modern minimally invasive technique that uses light to analyze the molecular composition of tissue, generating a unique "fingerprint" that reveals biochemical details, distinguishing between normal and diseased tissues.) when combined with Machine learning (ML) would provide accurate and expedite approach of detecting GC. We aim to meta-analyze the diagnostic accuracy of ML-enhanced RS in differentiating GC component from normal tissue. This study was conducted following PRISMA-DTA guidelines. We searched PubMed, Scopus, Web of Science, VHL, and Google Scholar up to the end of February 2025. with an updated search conducted on 14 July 2025. We included any peer-reviewed manuscript that assessed ML-based RS technique for detecting GC components against normal control during endoscopy and reported sufficient data to construct 2 × 2 contingency table for assessing basic diagnostic metrics such as the sensitivity and specificity were included. Methodological quality of studies deemed eligible was assessed using QUADAS-2 risk of bias tool. Data on true positives, true negatives, false positives, and false negatives were extracted to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC) using R software. Heterogeneity was assessed with I