Abstract Background 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. Methods 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 (PRISMA) guidelines. Eligible studies assessed LLM applications in nephrology. PROSPERO registration number: CRD42024550169. Results Of 1 070 records screened, 23 studies met inclusion criteria, addressing four clinical applications in nephrology. In patient education (n = 13), GPT-4 improved the readability of kidney donation information from a 10th to 4th grade level (9.6 ± 1.9 to 4.30 ± 1.71), and Gemini provided the most accurate answers to chronic kidney disease questions (GQS 3.46 ± 0.55). Regarding workflow optimisation (n = 7), GPT-4 achieved high accuracy (90–94%) 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 = 2), 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 = 1), Copilot significantly outperformed ChatGPT and Gemini in simulated nephrology datasets (median scores 5/5 compared with 4/5 and 4/5, p 0.01). TRIPOD-LLM assessment revealed frequent omissions in data handling, prompting strategies, and transparency. Conclusions While LLMs may enhance various aspects of nephrology practice, their widespread adoption remains premature. Input-quality dependence and limited external validation restrict generalisability. Further research is needed to confirm their real-world feasibility and ensure safe clinical integration.
Unger et al. (2025) studied this question.