{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T19:04:27Z","timestamp":1789585467580,"version":"build-2803163510"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To examine the evolving role and application of SNOMED CT (SCT) during 2020-2025, a period marked by the COVID-19 pandemic and accelerated adoption of artificial intelligence (AI) in healthcare.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We searched PubMed and Embase for articles published from October 2020 to June 2025. Included articles were classified into focus categories by implementation maturity (Theoretical, Predevelopment, Implementation, Evaluation\/Commodity, and Non-operational) and usage categories. We compared trends with our previous review covering January 2015-September 2020.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Following exclusion criteria, 651 articles were included for final review. The United States (n\u2009=\u2009188) and United Kingdom (n\u2009=\u200992) were the largest contributors. COVID-19 emerged as the second most-investigated domain. The 2020-2025 period witnessed a dramatic shift toward mature implementation stages: Implementation (26.4%) and Evaluation\/Commodity (32.0%) categories expanded, while Theoretical and Predevelopment categories decreased. SCT was increasingly used for knowledge graph construction, machine learning model validation, and patient data retrieval from registries. The most prominent use case involved retrieving patient data from national and commercial registries (n\u2009=\u2009186).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>SCT\u2019s role evolved from a clinical terminology to a machine-interpretable biomedical knowledge base, supporting explainable AI. However, coding consistency remains challenging, and evidence demonstrating improved patient outcomes is lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>SCT use has matured significantly, with widespread implementation in data repositories and emerging applications in AI. Future research must demonstrate clinical and operational benefits to ensure continued adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag094","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:46:41Z","timestamp":1779882401000},"page":"1798-1810","source":"Crossref","is-referenced-by-count":0,"title":["Use of SNOMED CT, 2020-2025: literature review"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1350-3606","authenticated-orcid":false,"given":"Eunsuk","family":"Chang","sequence":"first","affiliation":[{"name":"Republic of Korea Air Force Aerospace Medicine Research Center , 6 Ssangsugwangi-gil, Sangdang-gu , Cheongju-si, Chungcheongbuk-do, 28187,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javed","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Faculty of Information, University of Toronto , 140 St George St , 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