Clinical Applications and Limitations of Large Language Models in Nephrology: A Systematic Review
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Background
Large Language Models (LLMs) are emerging as promising tools in healthcare. This systematic review examines LLMs’ potential applications in nephrology, highlighting their benefits and limitations.
Methods
We conducted a literature search in PubMed and Web of Science, selecting studies based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review focuses on the latest advancements of LLMs in nephrology from 2020 to 2024. PROSPERO registration number: CRD42024550169.
Results
Fourteen studies met the inclusion criteria and were categorized into five key areas of nephrology: Streamlining workflow, disease prediction and prognosis, laboratory data interpretation and management, renal dietary management, and patient education. LLMs showed high performance in various clinical tasks, including managing continuous renal replacement therapy (CRRT) alarms (GPT-4 accuracy 90-94%) for reducing intensive care unit (ICU) alarm fatigue, and predicting chronic kidney diseases (CKD) progression (improved positive predictive value from 6.7% to 20.9%). In patient education, GPT-4 excelled at simplifying medical information by reducing readability complexity, and accurately translating kidney transplant resources. Gemini provided the most accurate responses to frequently asked questions (FAQs) about CKD.
Conclusions
While the incorporation of LLMs in nephrology shows promise across various levels of patient care, their broad implementation is still premature. Further research is required to validate these tools in terms of accuracy, rare and critical conditions, and real-world performance.
