Clinical Applications and Limitations of Large Language Models in Nephrology: A Systematic Review

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.

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.

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.

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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was conducted without financial support from any funding agencies in the public, commercial, or nonprofit sectors. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability As this article is a systematic review, no new data was generated. Instead, previously published data were analyzed. All data related to this systematic review is included in this published article and its supplementary materials. Any additional data are available upon reasonable request to the authors.