{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:07:26Z","timestamp":1789016846590,"version":"build-2803163510"},"reference-count":112,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Antimicrobial resistance (AMR) is a growing global health threat driven in part by inappropriate and unnecessary antibiotic use resulting from diagnostic uncertainty at the point of care. In outpatient and acute-care settings, clinicians are often unable to rapidly distinguish between viral and bacterial infections, leading to empiric antibiotic prescribing that contributes to the emergence and spread of resistant pathogens. This review examines current and emerging rapid diagnostic technologies for differentiating bacterial and viral infections, including molecular assays, rapid antigen tests, biomarker-based diagnostics, host-response platforms, hematologic methods, and artificial intelligence-based decision-support systems. These technologies are evaluated based on diagnostic accuracy, turnaround time, cost, accessibility, and clinical actionability within real-world healthcare settings. Although several emerging and point-of-care (POC) technologies can provide results within approximately 6\u201315 min, their ability to consistently align with the timing, workflow, and clinical decision-making requirements of frontline outpatient and emergency-care settings remains variable and incompletely established. Future progress in antimicrobial stewardship will depend on developing rapid, clinically actionable diagnostic systems that integrate seamlessly into patient care and reduce unnecessary antibiotic use.<\/jats:p>","DOI":"10.3390\/bios16090499","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:30:46Z","timestamp":1788777046000},"page":"499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid Diagnostics for Distinguishing Bacterial and Viral Infections: A Review of Technologies, Clinical Utility, and Stewardship Implications"],"prefix":"10.3390","volume":"16","author":[{"given":"Mohammad","family":"Javanmard","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Vellanki","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9737","authenticated-orcid":false,"given":"Ali","family":"Fardoost","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Javanmard","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"The Lancet Child & Adolescent Health (2024). 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