Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.

In precision phage therapy, artificial intelligence and machine learning (AI/ML) is being applied for phage detection, genomes/proteome annotation, host prediction, resistance profiling and therapeutic optimization. Yet the landscape remains fragmented owing to a lack of systematic synthesis of its translational maturity, methodological robustness and pipeline coverage. We performed a systematic review of AI/ML tools relevant to precision phage therapy. Web of Science, PubMed, MEDLINE, Scopus and IEEE Xplore were used to identify records. Of 6,969 identified records, 2,214 duplicates were removed, leaving 4,755 unique records for title/abstract screening; 510 full-text articles were assessed for eligibility, and 128 studies were included in the final synthesis. Studies were classified across a nine-module precision phage therapy pipeline by architectural family, validation maturity, accessibility, translational readiness (AI_PTRL), and methodological quality using an adapted PROBAST framework. The 128 included studies were published between 2012 and 2025, with a median publication year of 2023 and peak activity during 2022-2025. The large majority were methodological/tool development studies (115/128, 89.8%) and these predominantly described