{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:39:45Z","timestamp":1792384785453,"version":"4.4.0"},"reference-count":63,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Antimicrobial resistance (AMR) is one of the most urgent global public health threats, especially in low\u2010 and middle\u2010income countries. In 2019, bacterial AMR was estimated to have directly caused approximately 1.27 million deaths and to have been associated with 4.95 million deaths worldwide. Rapid identification of resistant infections is important, yet established diagnostics are very slow. In recent years, artificial intelligence (AI) has revolutionized microbiology and infectious disease diagnostics. This review explores the space of AI\u2010enabled diagnostics for AMR detection and characterization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This literature review was conducted from January to September 2026 using PubMed\/MEDLINE, Google Scholar, and other electronic databases with keywords including \u201cAI,\u201d \u201cAMR,\u201d \u201cdeep learning,\u201d \u201cgenomic diagnostics,\u201d and \u201cpoint\u2010of\u2010care (POC) testing.\u201d Following screening and full\u2010text assessment, 67 Peer\u2010reviewed articles and systematic reviews were included to support the synthesis presented in the manuscript, while non\u2010peer\u2010reviewed articles and studies lacking direct AI relevance were excluded. Titles and abstracts were screened before full\u2010text review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI\u2010enhanced diagnostics were reported to shorten turnaround time for AMR detection with promising but heterogeneous predictive performance; for example, pooled estimates across antimicrobial stewardship applications indicate an area under the curve of approximately 0.72. They also support improving antimicrobial stewardship and enable early therapeutic optimization in laboratory and POC settings, enabling real\u2010time clinical decision\u2010making. Despite these achievements, evidence for direct improvements in patient outcomes and routine bedside effectiveness remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010driven diagnostic tools represent a promising strategy to bridge laboratory innovation and bedside application in combating AMR. The tools have the capacity to improve patient outcomes and enhance antimicrobial stewardship as they can accurately detect resistance patterns and support real\u2010time clinical decisions. However, long\u2010term sustainability, long\u2010term success, and global impact can be achieved by continued validation, regulatory alignment, and equitable implementation across diverse healthcare settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.73396","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T13:23:52Z","timestamp":1791984232000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1722-3986","authenticated-orcid":false,"given":"Nadine","family":"Mugisha","sequence":"first","affiliation":[{"name":"Department of Research and Education Oli Health Magazine Organization Kigali Rwanda"},{"name":"Institute of Global Health Equity Research University of Global Health Equity Kigali Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4221-1474","authenticated-orcid":false,"given":"Lolita","family":"Fawaz","sequence":"additional","affiliation":[{"name":"Department of Research and Education Oli Health Magazine Organization Kigali Rwanda"},{"name":"Faculty of Dental Medicine University of Beirut Saint Joseph Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rawan","family":"Noureddine","sequence":"additional","affiliation":[{"name":"Department of Research and Education Oli Health Magazine Organization Kigali Rwanda"},{"name":"Gilbert and Rose\u2010Marie Chagoury School of Medicine Lebanese American University Beirut Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8820-3801","authenticated-orcid":false,"given":"Pascal Mathew","family":"Okorobe","sequence":"additional","affiliation":[{"name":"Department of Research and Education Oli 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