A Review on the Use of Artificial Intelligence for Human Microbiota Analysis in Clinical Tasks

This review briefly investigates how artificial intelligence (AI) methods are being applied to microbiota-based clinical tasks. We analyse a curated set of peer-reviewed studies to characterize the models used, the nature of microbiota-derived inputs, and the clinical goals addressed. Our findings show a preference for classical machine learning approaches, especially random forests, due to their robustness and interpretability. Deep learning methods are less frequent and primarily employed in multimodal contexts. Most studies focus on disease prediction or classification, though some explore treatment response or drug-microbiota interactions. Gut-derived profiles dominate the input data, with limited exploration of other microbiota niches. Key challenges include the lack of external validation, inconsistent preprocessing practices, and limited use of explainability techniques. These observations point to the need for more standardized, transparent, and clinically grounded research to advance the integration of AI with microbiome science.