{"title":"[Generative artificial intelligence ChatGPT in clinical nutrition - Advances and challenges].","authors":"Daniel de Luis Román","doi":"10.20960/nh.05692","DOIUrl":null,"url":null,"abstract":"<p><p>ChatGPT and other artificial intelligence (AI) tools can modify nutritional management in clinical settings. These technologies, based on machine learning and deep learning, enable the identification of risks, the proposal of personalized interventions, and the monitoring of patient progress using data extracted from clinical records. ChatGPT excels in areas such as nutritional assessment by calculating caloric needs and suggesting nutrient-rich foods, and in diagnosis, by identifying nutritional issues with technical terminology. In interventions, it offers dietary and educational strategies but lacks critical abilities such as interpreting non-verbal cues or performing physical examinations. Recent studies indicate that ChatGPT achieves high accuracy in questions related to clinical guidelines but shows deficiencies in integrating multiple medical conditions or ensuring the accuracy of meal plans. Additionally, generated plans may exhibit significant caloric deviations and imbalances in micronutrients such as vitamin D and B12. Despite its limitations, this AI has the potential to complement clinical practice by improving accessibility and personalization in nutritional care. However, its effective implementation requires professional supervision, integration with existing healthcare systems, and constant updates to its databases. In conclusion, while it does not replace nutrition experts, ChatGPT can serve as a valuable tool to optimize nutrition education and management of our patiens, always under the guidance of trained professionals.</p>","PeriodicalId":19385,"journal":{"name":"Nutricion hospitalaria","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutricion hospitalaria","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.20960/nh.05692","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
ChatGPT and other artificial intelligence (AI) tools can modify nutritional management in clinical settings. These technologies, based on machine learning and deep learning, enable the identification of risks, the proposal of personalized interventions, and the monitoring of patient progress using data extracted from clinical records. ChatGPT excels in areas such as nutritional assessment by calculating caloric needs and suggesting nutrient-rich foods, and in diagnosis, by identifying nutritional issues with technical terminology. In interventions, it offers dietary and educational strategies but lacks critical abilities such as interpreting non-verbal cues or performing physical examinations. Recent studies indicate that ChatGPT achieves high accuracy in questions related to clinical guidelines but shows deficiencies in integrating multiple medical conditions or ensuring the accuracy of meal plans. Additionally, generated plans may exhibit significant caloric deviations and imbalances in micronutrients such as vitamin D and B12. Despite its limitations, this AI has the potential to complement clinical practice by improving accessibility and personalization in nutritional care. However, its effective implementation requires professional supervision, integration with existing healthcare systems, and constant updates to its databases. In conclusion, while it does not replace nutrition experts, ChatGPT can serve as a valuable tool to optimize nutrition education and management of our patiens, always under the guidance of trained professionals.
期刊介绍:
The journal Nutrición Hospitalaria was born following the SENPE Bulletin (1981-1983) and the SENPE journal (1984-1985). It is the official organ of expression of the Spanish Society of Clinical Nutrition and Metabolism. Throughout its 36 years of existence has been adapting to the rhythms and demands set by the scientific community and the trends of the editorial processes, being its most recent milestone the achievement of Impact Factor (JCR) in 2009. Its content covers the fields of the sciences of nutrition, with special emphasis on nutritional support.