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ORIGINAL RESEARCH article

Front. Med.
Sec. Healthcare Professions Education
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1469227
This article is part of the Research Topic Large Language Models for Medical Applications View all 5 articles

Enhancing Nutritional Management in Peritoneal Dialysis Patients Through a Generative Pre-trained Transformers-Based Recipe Generation Tool: A Pilot Study

Provisionally accepted
Haijiao Jin Haijiao Jin 1,2Lulu Huang Lulu Huang 2Jinling Ye Jinling Ye 2Jinkun Wang Jinkun Wang 2Xinghui Lin Xinghui Lin 1,2Shaun Wu Shaun Wu 3Weiguo Hu Weiguo Hu 4Qisheng Lin Qisheng Lin 1,2Li Xiaoyang Li Xiaoyang 4*
  • 1 Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
  • 2 Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang Province, China
  • 3 WORK Medical Technology Group LTD, Hangzhou, China
  • 4 Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

The final, formatted version of the article will be published soon.

    Background: Patients undergoing peritoneal dialysis (PD) often face nutritional deficiencies due to inadequate intake, nutrient loss, insufficient dialysis, and a state of micro-inflammatory. Traditional nutritional management methods have not fully met personalized needs. Therefore, this study aimed to develop and evaluate an application for generating recipes based on Generative Pre-trained Transformers to improve the nutritional status of these patients.: This self-controlled prospective study included 35 patients undergoing PD from January to February 2024. The study was divided into two phases: the initial phase involved conventional dietary education under PD management, followed by a second phase where a new GPT-based dietary guidance tool was introduced. Patients adhered to the diets recommended by the tool. Nutritional intervention effects were assessed by comparing serum prealbumin, albumin, and phosphate levels before and after the intervention.Results: After the intervention, the mean prealbumin levels significantly improved from 289.04 ± 74.60 mg/L to 326.72 ± 78.89 mg/L (p=0.001). Although there was no statistical significance, the serum albumin levels in patients increased from 34.70 ± 5.94 g/L to 35.66 ± 5.14 g/L (p=0.153). Serum phosphate levels remained stable and within safe limits (p=0.241).The AI-based recipe generation application significantly improved serum prealbumin levels in PD patients without causing adverse changes in phosphate levels, confirming its efficacy and safety in nutritional management for these patients.This study highlights the potential and practical value of AI technology in nutritional management for patients with chronic disease, providing important evidence for future clinical applications.

    Keywords: artificial intelligence, Peritoneal Dialysis, Nutritional management, Generative Pre-trained Transformers system, Recipe generation

    Received: 23 Jul 2024; Accepted: 09 Oct 2024.

    Copyright: © 2024 Jin, Huang, Ye, Wang, Lin, Wu, Hu, Lin and Xiaoyang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Li Xiaoyang, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.