{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T08:29:05Z","timestamp":1786436945933,"version":"build-2736575974"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This perspective examines how artificial intelligence (AI) may reshape nephrology over the next two decades while keeping the nephrologist\u2019s role central. Prediction models for acute kidney injury and chronic kidney disease progression, and multimodal tools such as KidneyIntelX, will deliver continuous, patient-level risk estimates from electronic health records, biomarkers, imaging, and wearable devices. Large language models (LLMs) are \u201ccopilots\u201d for documentation, triage, education, and patient counseling, with potential to reduce administrative burden but also risks of hallucinations, bias, and uneven accuracy. Nephrologists will need new competencies in model calibration, fairness, communication, and ethics to decide when to follow or override algorithmic advice, especially for older, frail, and multimorbid patients. Overall, AI can support proactive, person-centred kidney care only if clinicians help design, govern, evaluate, and critically supervise these emerging technologies within robust, learning healthcare systems.<\/jats:p>","DOI":"10.1093\/joneph\/aajag216","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:49:49Z","timestamp":1785930589000},"source":"Crossref","is-referenced-by-count":0,"title":["The nephrologist in the present and near future: between algorithms and autonomy"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-1996","authenticated-orcid":false,"given":"Carmine","family":"Zoccali","sequence":"first","affiliation":[{"name":"Renal Research Institute , New York, NY \u00a0","place":["USA"]},{"name":"Institute of Molecular Biology and Genetics , Ariano Irpino ,","place":["Italy"]},{"name":"Associazione Ipertensione Nefrologia Trapianto Renal, c\/o Nefrologia, Grande Ospedale Metropolitano , 89124 Reggio Calabria ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Mallamaci","sequence":"additional","affiliation":[{"name":"Associazione Ipertensione Nefrologia Trapianto Renal, c\/o Nefrologia, Grande Ospedale Metropolitano , 89124 Reggio Calabria ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,11]]},"container-title":["Journal of Nephrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/joneph\/advance-article-pdf\/doi\/10.1093\/joneph\/aajag216\/70607488\/aajag216.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/joneph\/advance-article-pdf\/doi\/10.1093\/joneph\/aajag216\/70607488\/aajag216.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T07:47:10Z","timestamp":1786434430000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/joneph\/advance-article\/doi\/10.1093\/joneph\/aajag216\/8758732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/joneph\/aajag216","relation":{},"ISSN":["1121-8428","1724-6059"],"issn-type":[{"value":"1121-8428","type":"print"},{"value":"1724-6059","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"article-number":"aajag216"}}