{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:52:46Z","timestamp":1790056366015,"version":"4.0.1"},"reference-count":51,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Nursing"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The COVID-19 pandemic exposed systemic vulnerabilities in public health infrastructure, underscoring the urgency for innovation in disease surveillance and emergency response. Artificial intelligence (AI) has emerged as a promising tool to enhance the accuracy, efficiency, and scalability of public health interventions. Yet, there remains a limited understanding of how AI has been applied in real-world infectious disease control and who is contributing to its development and implementation.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This scoping review aimed to map current applications of AI in public health practice for infectious disease control since 2020. Specifically, it examined (1) the types of AI tools in use, (2) their purposes and implementation contexts, and (3) the professional and institutional actors leading these efforts, including the role of nurses.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Using the Joanna Briggs Institute\u2019s population, concept, and context framework, a structured search in Ovid MEDLINE was conducted, which was guided by the \u201c5Cs\u201d framework for health emergency preparedness from the World Health Organization (WHO). The search focused on English-language, peer-reviewed studies from 2020 that used AI tools for infectious disease control within real-world public health practice. Nonoriginal articles, simulation-only studies, and studies that lacked real-world implementation were excluded.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Out of 600 screened studies in Ovid MEDLINE, 10 met the inclusion criteria. Two major AI types were identified: machine learning (ML) algorithms and language-based tools such as chatbots and large language models. ML tools supported outbreak detection, risk stratification, and resource allocation, while language-based tools promoted health communication, particularly around immunization and HIV prevention. Studies were conducted in a diverse range of countries, including several low- and middle-income countries, and used national datasets or surveillance systems. Despite nurses comprising half of the global health workforce, no nursing-affiliated authors were found among first or corresponding authors, and no nurses were represented in the broader authorship of the included studies.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n AI technologies are being increasingly applied to support public health responses to infectious diseases, with applications ranging from predictive analytics to real-time public engagement. However, adoption remains limited in scale, scope, and professional diversity. The near-total absence of nursing participation in AI-related public health research is particularly striking and represents a missed opportunity for inclusive innovation. Strengthening implementation research and advancing informatics education among nursing professionals are critical next steps to ensure that AI tools reflect the realities of public health practice and promote equitable outcomes.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/84242","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T20:13:16Z","timestamp":1763410396000},"page":"e84242-e84242","source":"Crossref","is-referenced-by-count":5,"title":["Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0441-4107","authenticated-orcid":false,"given":"Chanhee","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1993-4623","authenticated-orcid":false,"given":"Robin","family":"Austin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0501-7317","authenticated-orcid":false,"given":"Rebecca","family":"Wurtz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7726-3640","authenticated-orcid":false,"given":"Connie White","family":"Delaney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5941-7500","authenticated-orcid":false,"given":"Sripriya","family":"Rajamani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"R1","unstructured":"WHO COVID-19 dashboard: deaths. 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