{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:00:21Z","timestamp":1793318421950,"version":"4.5.0"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML\/AI in the study, development and deployment of phage therapy.<\/jats:p>","DOI":"10.3390\/antibiotics14090949","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T10:08:58Z","timestamp":1758276538000},"page":"949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Smart Phages: Leveraging Artificial Intelligence to Tackle Prosthetic Joint Infections"],"prefix":"10.3390","volume":"14","author":[{"given":"Nicita","family":"Mehta","sequence":"first","affiliation":[{"name":"Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7478-2269","authenticated-orcid":false,"given":"Andrew T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8217-0593","authenticated-orcid":false,"given":"Edward K.","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Carl J. 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Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA"},{"name":"Harvard Combined Orthopedic Residency Program, 55 Fruit Street, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis","volume":"399","author":"Murray","year":"2022","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1004264","DOI":"10.1371\/journal.pmed.1004264","article-title":"Antimicrobial Resistance: Addressing a Global Threat to Humanity","volume":"20","author":"Walsh","year":"2023","journal-title":"PLoS Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.18081\/2333-5106\/2024.12\/151","article-title":"Comprehensive assessment of the global burden of antimicrobial resistance: Trends and insights from 2000 to 2023","volume":"12","author":"Shamiah","year":"2024","journal-title":"Am. 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