Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Objective: Prominent large language models, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have shown promising implementation in the field of nutrition. Special care should be taken when using ChatGPT to prescribe protein-restricted diets for kidney-impaired patients. The objective of the current study is to simulate a chronic kidney disease (CKD) patient and evaluate the capabilities of ChatGPT in the context of dietary prescription, with a focus on protein contents of the diet.
Methods: We simulated a scenario involving a CKD patient and replicated a clinical counseling session that covered general dietary principles, dietary assessment, energy and protein recommendation, dietary prescription, and diet customization based on dietary culture. To confirm the results derived from our qualitative observations, 10 colleagues were recruited and provided with identical dietary prescription prompts to run the process again. The actual energy and protein levels of the given meal plans were recorded and the difference from the targets were compared.
Results: ChatGPT provides general principles overall aligning with best practices. The recommendations for energy and protein requirements of CKD patients were tailored and satisfactory. It failed to prescribe a reliable diet based on the target energy and protein requirements. For the quantitative analysis, the prescribed energy levels were generally lower than the targets, ranging from -28.9% to -17.0%, and protein contents were tremendously higher than the targets, ranging from 59.3% to 157%.
Conclusion: ChatGPT is competent in offering generic dietary advice, giving satisfactory nutrients recommendations and adapting cuisines to different cultures but failed to prescribe nutritionally accurate dietary plans for CKD patients. At present, patients with strict protein and other particular nutrient restrictions are not recommended to rely on the dietary plans prescribed by ChatGPT to avoid potential health risks.
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http://dx.doi.org/10.1053/j.jrn.2025.02.008 | DOI Listing |