{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:05:45Z","timestamp":1792515945808,"version":"4.5.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"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":[[2026,1,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the potential of artificial intelligence (AI) to enhance urine cytopathology for detecting urothelial carcinoma (UC), emphasizing improvements in diagnostic sensitivity, accuracy, and efficiency, as well as potential reductions in pathologist workload.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Google Scholar, EMBASE, and ScienceDirect were searched (January 2018 to July 2025) for English-language studies applying AI to urine cytology for UC detection and reporting sensitivity and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eleven studies met the inclusion criteria, with sample sizes ranging from 116 to 2641 cases. The AI models, predominantly convolutional neural networks, achieved a sensitivity of 63% to 100% and a specificity of 61.8% to 100% for high-grade urothelial carcinoma (HGUC) detection. Artificial intelligence has the potential to improve detection and streamline workflows in clinical settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Artificial intelligence shows strong potential as a diagnostic aid in urine cytopathology, particularly for HGUC detection, by improving accuracy and efficiency. 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