{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T23:39:46Z","timestamp":1792366786983,"version":"4.4.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) offers considerable promise for strengthening governmental public health decision making by supporting rapid, comprehensive analysis of complex data. Although AI applications have been widely examined in clinical and academic settings, their use in public health agencies and policymaking remains less well understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This scoping review assessed how AI has been applied to support decision making by public health professionals and elected officials in both routine and crisis contexts. Using PRISMA-ScR guidelines, we searched PubMed, OAISTER, and Web of Science for literature published between 2014 and 2024. From 13,239 records identified, seven studies met final inclusion criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The identified evidence base is primarily descriptive and exploratory, with limited empirical evaluation of outcomes or effectiveness. All included studies described AI use during the COVID-19 pandemic, focusing on vaccination decision support, contact tracing, quarantine enforcement, and\/or movement restrictions, which limits generalizability to other public health contexts and decision-making scenarios. Findings highlight a small but emerging evidence base, with most applications developed in response to emergencies rather than embedded in routine practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Future opportunities for AI include advancing surveillance, communication, and resource allocation. However, critical challenges remain regarding governance, equity, and implementation. Further research is needed to evaluate AI interventions in diverse contexts and establish sustainable pathways for adoption by governmental public health agencies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1745684","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T06:47:54Z","timestamp":1772520474000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Impact, use, and implications of artificial intelligence in public health decision making by elected officials: a scoping review"],"prefix":"10.3389","volume":"14","author":[{"given":"Elizabeth A.","family":"Campbell","sequence":"first","affiliation":[{"name":"Johns Hopkins University Center for Health Security","place":["Baltimore, MD, United States"]},{"name":"Johns Hopkins Center for Outbreak Response Innovation","place":["Baltimore, MD, United 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