{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:52:44Z","timestamp":1783529564333,"version":"3.55.0"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder leading to kidney failure. Recent advancements in artificial intelligence (AI) are transforming the diagnosis, risk stratification, management and prognostication in ADPKD by enabling more accurate assessments and individualized care. AI-powered imaging tools enhance the measurement of total kidney volume (TKV), improving the precision and efficiency of monitoring disease progression and more reliable assessment of therapeutic response. Machine learning algorithms can integrate genetic, imaging and clinical data to predict kidney function decline, facilitating personalized treatment strategies. In addition, AI is being used to identify genetic variants and to refine genotype\u2013phenotype relationships, offering deeper insights into disease variability. AI-enabled monitoring technologies can support longitudinal tracking of TKV measurements obtained through magnetic resonance imaging or computed tomography, thereby improving clinical decision-making and management. Furthermore, AI can optimize clinical trial design by improving patient selection, prediction of treatment responses and safety monitoring. Despite these promising developments, integrating AI into the clinical practice remains controversial and may pose several challenges. This review highlights the emerging clinical applications of AI in ADPKD, emphasizing its potential to advance precision medicine and improve patient outcomes.<\/jats:p>","DOI":"10.1093\/ndt\/gfag010","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T12:42:08Z","timestamp":1769258528000},"page":"1204-1213","source":"Crossref","is-referenced-by-count":1,"title":["Clinical applications of artificial intelligence in autosomal dominant polycystic kidney disease"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-7820","authenticated-orcid":false,"given":"Niloufar","family":"Ebrahimi","sequence":"first","affiliation":[{"name":"Department of Medicine, Division of Nephrology, Loma Linda University Medical Center , Loma Linda, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wisit","family":"Cheungpasitporn","sequence":"additional","affiliation":[{"name":"Division of Nephrology and 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