Artificial intelligence in oral microbiome and dental genomics research: an evidence matrix review

Artificial intelligence has spread rapidly through oral microbiome and dental genomics research, but the evidence is dispersed across imaging, sequencing and multi-omics studies of uneven methodological quality. This narrative review organizes the literature as an evidence matrix and synthesizes the applications, the quality of the evidence and its consistency across three domains: machine learning applied to the oral microbiome, machine learning applied to dental genomics, and the integration of both into clinical prediction. Sources were searched in Scopus, PubMed and Crossref, and studies were extracted into a matrix with study, design, population, finding and quality. Of the fourteen studies that applied machine learning to the oral microbiome, supervised classification and regression predominated, with modest to good discrimination for age, malodour, substance use and disease status. Of the seven studies in the dental-genomics domain, genome-wide and transcriptome-wide approaches identified candidate genes and loci, but shared prioritization between systemic and periodontal traits remained limited. Of the eighteen studies in the diagnostic-imaging domain, convolutional networks achieved the highest reported accuracy for caries and oral cancer, although few studies used public datasets or external validation. The evidence supports feasibility rather than clinical readiness; methodological heterogeneity, small samples and scarce external validation were the most frequent limitations.