Applications of Artificial Intelligence in Bacteriophage‑Based Treatment of Antibiotic‑Resistant Infections

Antibiotic‑resistant infections have emerged as a major global health crisis, driving the need for alternative therapeutic strategies beyond conventional antimicrobials. Bacteriophage therapy offers a targeted and adaptable approach for treating multidrug‑resistant (MDR) pathogens; however, its clinical implementation remains limited by challenges in phage selection, host‑range prediction, and the design of effective therapeutic combinations. This review aims to evaluate the current applications of artificial intelligence (AI) in optimizing bacteriophage‑based treatments for antibiotic‑resistant infections. Recent advances demonstrate that AI‑driven models can accurately predict phage–host interactions, identify lytic phages within large genomic datasets, and support the rational design of phage cocktails to minimize resistance development. Additionally, machine learning algorithms facilitate the engineering of phages with enhanced specificity, while computational frameworks improve clinical decision‑making through real‑time analysis of microbiological and patient‑level data. The results of this synthesis highlight the transformative role of AI in advancing precision phage therapy. In conclusion, integrating AI with bacteriophage research holds significant potential for accelerating therapeutic development, improving treatment efficacy, and supporting the broader clinical adoption of phage‑based interventions against antibiotic‑resistant infections.

کلیدواژه ها:

Artificial intelligence ، Bacteriophage therapy ، Antibiotic resistance ، Machine learning ، Phage–host prediction ، Phage cocktails ، Genome engineering ، Precision medicine