• Large language models in nephr...

*Result*: Large language models in nephrology: applications and challenges in chronic kidney disease management.

Title:
Large language models in nephrology: applications and challenges in chronic kidney disease management.
Authors:
Hu Y; Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China., Liu J; Department of medical administration, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China., Jiang W; Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Source:
Renal failure [Ren Fail] 2025 Dec; Vol. 47 (1), pp. 2555686. Date of Electronic Publication: 2025 Sep 07.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Informa Healthcare Country of Publication: England NLM ID: 8701128 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-6049 (Electronic) Linking ISSN: 0886022X NLM ISO Abbreviation: Ren Fail Subsets: MEDLINE
Imprint Name(s):
Publication: London : Informa Healthcare
Original Publication: New York, N.Y. : M. Dekker, c1987-
MeSH Terms:
Renal Insufficiency, Chronic*/therapy , Nephrology*/methods , Artificial Intelligence*, Humans ; Patient Education as Topic ; Disease Management ; Health Literacy ; Language ; Large Language Models
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Contributed Indexing:
Keywords: CKD; Large language models (LLMs); artificial intelligence (AI); clinical decision support; digital health
Entry Date(s):
Date Created: 20250908 Date Completed: 20250908 Latest Revision: 20250911
Update Code:
20260130
PubMed Central ID:
PMC12418797
DOI:
10.1080/0886022X.2025.2555686
PMID:
40916423
Database:
MEDLINE

*Further Information*

*Large language models (LLMs) represent a transformative advance in artificial intelligence, with growing potential to impact chronic kidney disease (CKD) management. CKD is a complex, highly prevalent condition requiring multifaceted care and substantial patient engagement. Recent developments in LLMs-including conversational AI, multimodal integration, and autonomous agents-offer novel opportunities to enhance patient education, streamline clinical documentation, and support decision-making across nephrology practice. Early reports suggest that LLMs can improve health literacy, facilitate adherence to complex treatment regimens, and reduce administrative burdens for clinicians. However, the rapid deployment of these technologies raises important challenges, including patient privacy, data security, model accuracy, algorithmic bias, and ethical accountability. Moreover, real-world evidence supporting the safety and effectiveness of LLMs in nephrology remains limited. Addressing these challenges will require rigorous validation, robust regulatory frameworks, and ongoing collaboration between clinicians, AI developers, and patients. As LLMs continue to evolve, future efforts should focus on the development of nephrology-specific models, prospective clinical trials, and strategies to ensure equitable and transparent implementation. If appropriately integrated, LLMs have the potential to reshape the landscape of CKD care and education, improving outcomes for patients and supporting the nephrology workforce in an era of increasing complexity.*