Clinical applications and limitations of large language models in nephrology: a systematic review.

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 guidelines. Eligible studies assessed LLM applications in nephrology. PROSPERO registration number CRD42024550169.

RESULTS

Of 1070 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 a 4th grade level (9.6 ± 1.9 to 4.30 ± 1.71) and Gemini provided the most accurate answers to chronic kidney disease questions (Global Quality Score 3.46 ± 0.55). Regarding workflow optimization (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 < .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 generalizability. Further research is needed to confirm their real-world feasibility and ensure safe clinical integration.