Evidence map›Paper›PMID 42370370›Full record

ArticleFrontiers in nutrition2026

Med-Diet: evaluation of an LLM-based system for clinically guided nutrition care in chronic diseases.

Yanan Wang, Miaomiao Cheng, Qi Zhang, Jian Feng, Jun Qin, Lei Gong, Lei Liu, Yan Zhao, Shasha Zhao, Fanli Meng and 9 more

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Yanan Wang *Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Miaomiao Cheng *Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Qi Zhang *Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Jian FengDepartment of Clinical Nutrition, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Jun QinDepartment of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Lei GongDepartment of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Lei LiuDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Yan ZhaoDepartment of Clinical Nutrition, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Shasha ZhaoDepartment of Clinical Nutrition, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Fanli MengDepartment of Hepatology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Longyan ChenDepartment of Hepatology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Tingjun DaiDepartment of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Pengfei LinDepartment of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Jie PengDepartment of Cardiology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Yun TiDepartment of Cardiology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Lijun SongDepartment of Rheumatology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Qi LiuDepartment of Rheumatology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Hua LiangDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Xiaoyan XiaoDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Scientifically grounded and clinically applicable dietary management is essential for patients with chronic diseases. However, in routine practice, nutritionists frequently lack efficient and scalable tools to deliver targeted, guideline-consistent nutritional guidance across diverse and complex clinical scenarios. Objective: To develop and conduct an exploratory expert-rating evaluation of Med-Diet, a large language model (LLM)-based agent for generating dietary plans for chronic diseases. Methods: We built Med-Diet using DeepSeek-R1, integrated clinical dietary guidelines, evaluated it on 79 real cases covering common, rare, and complex noncommunicable diseases, and compared it with four general-purpose LLMs (DeepSeek-R1, GPT-4o, GLM-Z1-32B, and Llama-3.3-70B). Fourteen clinical experts from different fields conducted blinded, multidimensional ratings of generated dietary plans. Furthermore, an exploratory comparative experiment assessed nutritionists' efficiency and output quality without and with Med-Diet assistance. Results: Med-Diet received higher mean preference scores from expert evaluators compared to all baseline LLMs (mean score of 4.09 ± 0.64). Expert ratings suggested superior performance for Med-Diet in dimensions including accuracy, safety, nutritional balance, personalization, practicality, and overall recommendation (all Conclusion: In this expert-rating study, Med-Diet-generated dietary plans received higher preference scores from clinical experts compared to those from general-purpose LLMs. These preliminary findings suggest that knowledge injection and framework constraints of Med-Diet may improve expert-perceived quality of AI-generated dietary plans. Med-Diet shows potential as an adjunctive tool in the dietary management of chronic diseases, but its clinical safety and effectiveness require prospective validation.

Indexed as

AI-based dietary planclinical nutritiondietary managementlarge language modelsnoncommunicable chronic diseases

Identifiers

PMID42370370
PMCPMC13303124

What Socratic holds

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LicenceCC BY
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Registered trials

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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.