ArticleBMC nephrology2026
Comparison of AI-generated renal diets by different large language models: a guideline-based evaluation with expert input.
Article in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic kidney disease (CKD) represents a major public health concern due to its increasing prevalence worldwide and the substantial burden it places on healthcare systems. Nutritional therapy plays a critical role in slowing the progression of the disease. Recently, large language models (LLMs) such as ChatGPT, Copilot, and Gemini have been introduced for dietary planning in patients with kidney disease. However, the accuracy, reliability, and guideline adherence of the diets generated by these models remain uncertain. This study aimed to evaluate AI-based dietary recommendations in light of clinical guidelines and expert opinions.
methodsThree different artificial intelligence models (ChatGPT-4o, Microsoft Copilot and Google Gemini and Microsoft Copilot) were tested using standardized patient scenarios in both Turkish and English. The models were asked to generate dietary plans for hemodialysis patients, CKD stage 3–5 patients, and a reference diet of 1800 kcal/40 g protein. The dietary outputs obtained were analyzed using the BeBIS 9 software and evaluated with IBM SPSS Statistics 26.0. Energy, macro- and micronutrient contents were compared against the TUBER 2022 (Türkiye Nutrition Guide 2022) and KDOQI (Kidney Disease Outcomes Quality Initiative) guidelines. In addition, five experts scored the models’ recommendations based on accuracy, comprehensiveness, reproducibility, innovation/personalization, and nutritional diversity/practicality.
resultsThe energy values generated by all models remained below the reference targets. Significant differences were observed in energy content across models and languages (lowest: Gemini-English: 859.11 ± 151.35 kcal vs. highest: Copilot-English:1496.03 ± 249.71 kcal; p < 0.001). Potassium levels varied significantly by model (p < 0.001), with some outputs exceeding safe limits. In expert evaluations for ‘Accuracy’, Gemini-English achieved the highest score (median: 4.00), whereas ChatGPT-Turkish recorded the lowest (median: 0.00). While Gemini stood out in innovation, Copilot-Turkish plans yielded results most closely aligned with the guidelines in terms of sodium and phosphorus. None of the models fully met the guideline requirements.
conclusionsAlthough AI-based large language models hold potential for dietary planning in patients with kidney disease, they demonstrate inconsistencies in nutrient accuracy, guideline adherence, and personalization. Their standalone use in clinical practice is therefore not appropriate; expert supervision and integration with clinical guidelines are required.
trial registrationNot applicable.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.