Artificial intelligence-driven strategies to enhance the application of lactic acid bacteria as functional probiotics: Health promotion and optimization for industrial applications

Abstract

Background

Lactic acid bacteria (LAB) are widely recognized for their probiotic properties and health-promoting effects, including modulation of gut microbiota, immune regulation, and metabolic support. However, traditional approaches to identifying and applying functional LAB strains are labor-intensive and limited in their scalability. With the growing demand for precision probiotics and microbiome-targeted therapies, there is an urgent need for advanced methods that can systematically optimize the functionalities of LAB.

Scope and approach

This review explores how artificial intelligence (AI) technologies, including machine learning, deep learning, and hybrid models, can enhance LAB-based probiotic research and applications. We categorize current AI applications across three major domains: (i) functional strain discovery, (ii) metabolic modeling and personalized health interventions, and (iii) industrial-scale formulation and quality control.

Key findings and conclusion

AI technologies enable accurate prediction of probiotic traits such as AMP production, SCFA synthesis, and host interaction potential. They also facilitate the discovery of functional metabolites and streamline industrial processes like fermentation and formulation. These advances support a shift toward data-driven, precision approaches in probiotic development. Continued progress will rely on standardized data, explainable models, and interdisciplinary integration.