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Electronic searches were conducted in PubMed, Embase, Web of Science, Scopus, and Google Scholar.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 28 studies published between 2009 and 2025 were included after screening 2,687 records. Most studies used static IRT (SIRT) or dynamic IRT (DIRT), many of them integrating artificial intelligence (AI) tools. The assessed studies demonstrated temperature differences between malignant nodules, benign nodules, and healthy tissues, particularly under thermal stress. AI classifiers, especially convolutional neural networks (CNN), have demonstrated enhanced diagnostic performance, with reported accuracies reaching up to 98.4%. However, results varied due to differences in imaging protocols, camera resolution, and patient-specific factors such as subcutaneous fat thickness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>IRT shows promising potential as an adjunctive diagnostic tool for assessing thyroid nodules, particularly when combined with AI and dynamic protocols. However, standardization and further validation in diverse populations are needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00405-026-10309-6","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T08:27:58Z","timestamp":1780388878000},"page":"5421-5438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The role of infrared thermography in thyroid nodule detection and classification: a scoping review"],"prefix":"10.1007","volume":"283","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6721-0745","authenticated-orcid":false,"given":"Diego Filipe Bezerra","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1826-9265","authenticated-orcid":false,"given":"Diana","family":"Lance","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0158-5085","authenticated-orcid":false,"given":"Elis \u00c2ngela","family":"Batistella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2380-109X","authenticated-orcid":false,"given":"Cassiano Francisco Weege","family":"Nonaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6477-6997","authenticated-orcid":false,"given":"Daniela Pita","family":"de Melo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"10309_CR1","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/S2213-8587(22)00035-3","volume":"10","author":"M Pizzato","year":"2022","unstructured":"Pizzato M, Li M, Vignat J, Laversanne M, Singh D, La Vecchia C, Vaccarella S (2022) The epidemiological landscape of thyroid cancer worldwide: GLOBOCAN estimates for incidence and mortality rates in 2020. 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