{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:03:04Z","timestamp":1784952184917,"version":"3.55.0"},"reference-count":69,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Cytopathology"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Nongynecologic (non\u2010GYN) cytology plays an important role in cellular and molecular cancer diagnosis, although its use is limited by variability and interobserver discrepancies. Recent studies suggest that artificial intelligence (AI) may improve diagnostic consistency and performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This systematic review evaluated contemporary AI research in non\u2010GYN cytology and examined commercially available systems. MEDLINE, Embase, and the Cochrane Library were searched for English\u2010language studies published from January 2010 to September 2024. Of ~24,000 records screened, 71 met inclusion criteria. Commercial platforms (KFBIO, Landing Med, VitaDx\/VisioCyt, AIxMED, and CellsVision) were assessed using publicly available regulatory documents, technical specifications, and vendor\u2010reported validation data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Many studies reported high internal diagnostic performance, often exceeding 90% accuracy. Patch\u2010level performance often reached ~94%, exceeding whole slide image\u2010level metrics. Reduced interobserver variability was frequently observed, with accuracies of 95.9% for senior and 94.4% for junior pathologists. Several studies documented shorter diagnostic time. Thyroid (37%) and urinary bladder (30%) cytology were most frequently studied. AI applications included categorization, segmentation, and atypia detection, with recent work exploring immunocytochemistry support, mutation\u2010associated pattern recognition, and indirect prediction of histologic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI in non\u2010GYN cytology shows promise but remains limited by data set quality, weak external validation, and regulatory barriers. At present, AI tools function primarily as decision\u2010support systems. Advancing clinical adoption will require multicenter validation, standardized data sets, and careful integration with expert interpretation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cncy.70092","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:18:52Z","timestamp":1774027132000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools"],"prefix":"10.1002","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9680-1525","authenticated-orcid":false,"given":"Tsandmaa","family":"Byambadorj","sequence":"first","affiliation":[{"name":"Department of Hospital Pathology College of Medicine The Catholic University of Korea  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Rizwan","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology College of Medicine The Catholic University of Korea  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yosep","family":"Chong","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology College of Medicine The Catholic University of Korea  Seoul Republic of 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