Artificial Intelligence and Bacteriophage Therapy: A Strategy to Combat Antimicrobial Resistance in Livestock

Recent advancements in artificial intelligence (AI) offer potential solutions for precision veterinary phage therapy. Current applications largely remain in the computational proof-of-concept or preclinical stages. Deep learning and genomic models have shown notable accuracy with benchmark datasets, while reinforcement learning has been investigated for optimizing phage cocktail strategies. AI-supported genomic screening may enhance biosafety assessments, and the integration with real-time AMR surveillance could aid in adaptive deployment strategies.

Economic modeling indicates that even modest reductions in the use of metaphylactic antibiotics could produce benefits for both production and public health, though these estimates are illustrative. This review consolidates the evidence on AI-driven phage discovery, epidemiological modeling, microbiome dynamics, risk assessment of horizontal gene transfer, economic analyses, and regulatory developments. In the One Health framework, the implementation of AI-guided phage platforms comprises a strategic opportunity to diminish reliance on antimicrobials, contingent on addressing critical knowledge gaps.

AMR in animal production systems is quantitatively substantial, with modeling estimates indicating that global antimicrobial use in livestock surpassed 93,000 tons in 2017. Critically important antimicrobials, such as macrolides and third-generation cephalosporins, are still extensively used in various livestock sectors, leading to cross-resistance risks at the human-animal interface. Resistant strains of pathogens linked to the use of antimicrobials in food animals have been identified, posing direct threats to food safety and public health.

Recent surveillance from low- and middle-income countries highlights troubling trends, with studies reporting high rates of antibiotic resistance among various livestock species. A meta-analysis shows a decline in clinical effectiveness of antibiotics, decreasing from approximately 85% in 2000 to 65% in 2018, varying significantly by region and pathogen-drug combination. Resistant outbreaks can lead to increased mortality, diminished production efficiency, and trade restrictions.

Bacteriophage therapy, while offering precise pathogen targeting, faces multiple barriers in livestock applications. AI provides computational methodologies that may address some of these limitations. However, the majority of AI approaches have not yet been validated for livestock-specific applications, particularly as most models have been tested on human-derived datasets.

Galaxy-level and biological models for phage-host interactions remain largely untested under field conditions. The research demonstrates a need for increased validation studies focusing on livestock-specific pathogens to determine efficacy and safety. As agricultural production systems apply interventions at the population level, AI-guided solutions may provide opportunities to enhance pathogen control amidst high-density livestock systems.

The economic impact of AMR in livestock could grow significantly without strategic intervention, which emphasizes the necessity for enhanced AI-guided phage therapies as part of an overarching strategy to curb antibiotic dependence in agriculture. Knowledge gaps concerning the ecological effects of phage therapy and the long-term impacts on AMR must be addressed through comprehensive studies and regulatory adaptations.

Future research should seek to establish standardized genomic safety criteria, facilitate international regulatory convergence, and pursue field trials for AI-guided phage therapies to optimize deployment toward production systems. A roadmap for research and implementation over the next decade has been proposed, focusing on essential infrastructure and international collaboration to advance these technologies.