Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine

Abstract

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine.

Keywords: artificial intelligence; autoimmune diseases; chronic inflammation; gastrointestinal disorders; gut microbiota; machine learning; metabolic diseases; probiotics.

Figure 1

AI-Driven workflow for precision medicine in microbiome research. Created with Biorender [39].

Figure 2

AI-driven microbiome analysis for precision medicine. Created with Biorender [30].

Figure 3

Overview of Machine-Learning approaches in data classification and decision-making. (A). Supervised learning: algorithms trained on labeled data to predict outcomes (e.g., Random Forest, SVM). (B). Unsupervised learning: algorithms detecting hidden patterns and clusters in unlabeled data (e.g., k-means clustering). (C). Semi-supervised learning: hybrid models combining small labeled datasets with large unlabeled datasets. (D). Reinforcement learning: agent–environment interactions optimized through rewards and penalties (*→First-level decision split; ** → Second-level decision split; *** → Third-level decision split; Circle: Class/category 1; Square: Class/category 2; Triangle: Class/category 3). (E). Deep-learning architectures: neural networks with multiple layers, useful for complex pattern recognition in high-dimensional data. (F). Dimensionality reduction/feature extraction: techniques such as PCA or autoencoders used to compress high-dimensional microbiome datasets into interpretable forms. Created with Biorender [39].