{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:44:47Z","timestamp":1788327887125,"version":"build-2803163510"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Large language models (LLMs) have emerged as potential tools in healthcare. This systematic review evaluates the applications of text-generative conversational LLMs in nephrology, with particular attention to their reported advantages and limitations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A systematic search was performed in PubMed, Web of Science, Embase and the Cochrane Library in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies assessed LLM applications in nephrology. PROSPERO registration number CRD42024550169.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of 1070 records screened, 23 studies met inclusion criteria, addressing four clinical applications in nephrology. In patient education (n\u00a0=\u00a013), GPT-4 improved the readability of kidney donation information from a 10th to a 4th grade level (9.6\u00a0\u00b1\u00a01.9 to 4.30\u00a0\u00b1\u00a01.71) and Gemini provided the most accurate answers to chronic kidney disease questions (Global Quality Score 3.46\u00a0\u00b1\u00a00.55). Regarding workflow optimization (n\u00a0=\u00a07), GPT-4 achieved high accuracy (90\u201394%) in managing continuous renal replacement therapy alarms and improved diagnosis of diabetes insipidus using chain-of-thought and retrieval-augmented prompting. In renal dietary guidance (n\u00a0=\u00a02), Bard AI led in classifying phosphorus and oxalate content of foods (100% and 84%), while GPT-4 and Bing Chat were most accurate for potassium classification (81%). For laboratory data interpretation (n\u00a0=\u00a01), Copilot significantly outperformed ChatGPT and Gemini in simulated nephrology datasets (median scores 5\/5 compared with 4\/5 and 4\/5; P\u00a0&amp;lt;\u00a0.01). TRIPOD-LLM assessment revealed frequent omissions in data handling, prompting strategies and transparency.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>While LLMs may enhance various aspects of nephrology practice, their widespread adoption remains premature. Input-quality dependence and limited external validation restrict generalizability. Further research is needed to confirm their real-world feasibility and ensure safe clinical integration.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ckj\/sfaf243","type":"journal-article","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:28:06Z","timestamp":1758328086000},"source":"Crossref","is-referenced-by-count":13,"title":["Clinical applications and limitations of large language models in nephrology: a systematic review"],"prefix":"10.1093","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8640-9479","authenticated-orcid":false,"given":"Zoe","family":"Unger","sequence":"first","affiliation":[{"name":"First Faculty of Medicine, Charles University , Prague ,","place":["Czech Republic"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7853-2029","authenticated-orcid":false,"given":"Shelly","family":"Soffer","sequence":"additional","affiliation":[{"name":"Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center , Petah-Tikva ,","place":["Israel"]},{"name":"School of Medicine, Tel Aviv University , Tel Aviv ,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6024-9110","authenticated-orcid":false,"given":"Orly","family":"Efros","sequence":"additional","affiliation":[{"name":"School of Medicine, Tel Aviv University , Tel Aviv ,","place":["Israel"]},{"name":"National Hemophilia Center and Thrombosis Institute, Sheba Medical Center , Ramat Gan ,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Chan","sequence":"additional","affiliation":[{"name":"Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai , New York, NY ,","place":["USA"]},{"name":"Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY ,","place":["USA"]},{"name":"Barbara T. 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