AI-Based Microbiome Data Interpretation

Abstract

The human microbiome comprises trillions of microorganisms whose collective metabolic activity influences health and disease, and AI models that interpret complex microbiome datasets to predict disease states, guide therapeutic interventions, and elucidate host-microbiome interactions now complement traditional ecological analyses, yet adoption varies across AI application categories. We evaluated 212 AI-based microbiome interpretation programmes across centres affiliated with Nordic Technical University in Stockholm between 2018 and 2022, spanning five application categories: disease state classification from microbiome profiles, microbiome-based biomarker discovery, metabolic function prediction from community composition, host-microbiome interaction modelling, and personalised microbiome intervention design. A Microbiome AI Interpretation Index (MAII) was constructed from five sub-scores -- classification accuracy across disease contexts, biomarker reproducibility, mechanistic interpretability, cross-cohort generalisation, and clinical actionability -- with weights from regression against sustained adoption. MAII correlated with adoption at r = +0.83 and discriminated adopted from non-adopted methods with an AUC of 0.880. Disease classification scored highest (mean MAII 0.822), while personalised intervention design trailed at 0.596. Only 34.4 percent exceeded the 0.75 threshold. Classification accuracy carried the largest weight (beta = +0.280), followed by cross-cohort generalisation (beta = +0.228).