PDF(Pubmed)
Abstract：
Nutritional management for patients with diabetes in China is a significant challenge due to the low supply of registered clinical dietitians. To address this, an artificial intelligence (AI)-based nutritionist program that uses advanced language and image recognition models was created. This program can identify ingredients from images of a patient\'s meal and offer nutritional guidance and dietary recommendations.
The primary objective of this study is to evaluate the competence of the models that support this program.
The potential of an AI nutritionist program for patients with type 2 diabetes mellitus (T2DM) was evaluated through a multistep process. First, a survey was conducted among patients with T2DM and endocrinologists to identify knowledge gaps in dietary practices. ChatGPT and GPT 4.0 were then tested through the Chinese Registered Dietitian Examination to assess their proficiency in providing evidence-based dietary advice. ChatGPT\'s responses to common questions about medical nutrition therapy were compared with expert responses by professional dietitians to evaluate its proficiency. The model\'s food recommendations were scrutinized for consistency with expert advice. A deep learning-based image recognition model was developed for food identification at the ingredient level, and its performance was compared with existing models. Finally, a user-friendly app was developed, integrating the capabilities of language and image recognition models to potentially improve care for patients with T2DM.
Most patients (182/206, 88.4%) demanded more immediate and comprehensive nutritional management and education. Both ChatGPT and GPT 4.0 passed the Chinese Registered Dietitian examination. ChatGPT\'s food recommendations were mainly in line with best practices, except for certain foods like root vegetables and dry beans. Professional dietitians\' reviews of ChatGPT\'s responses to common questions were largely positive, with 162 out of 168 providing favorable reviews. The multilabel image recognition model evaluation showed that the Dino V2 model achieved an average F1 score of 0.825, indicating high accuracy in recognizing ingredients.
The model evaluations were promising. The AI-based nutritionist program is now ready for a supervised pilot study.
The primary objective of this study is to evaluate the competence of the models that support this program.
The potential of an AI nutritionist program for patients with type 2 diabetes mellitus (T2DM) was evaluated through a multistep process. First, a survey was conducted among patients with T2DM and endocrinologists to identify knowledge gaps in dietary practices. ChatGPT and GPT 4.0 were then tested through the Chinese Registered Dietitian Examination to assess their proficiency in providing evidence-based dietary advice. ChatGPT\'s responses to common questions about medical nutrition therapy were compared with expert responses by professional dietitians to evaluate its proficiency. The model\'s food recommendations were scrutinized for consistency with expert advice. A deep learning-based image recognition model was developed for food identification at the ingredient level, and its performance was compared with existing models. Finally, a user-friendly app was developed, integrating the capabilities of language and image recognition models to potentially improve care for patients with T2DM.
Most patients (182/206, 88.4%) demanded more immediate and comprehensive nutritional management and education. Both ChatGPT and GPT 4.0 passed the Chinese Registered Dietitian examination. ChatGPT\'s food recommendations were mainly in line with best practices, except for certain foods like root vegetables and dry beans. Professional dietitians\' reviews of ChatGPT\'s responses to common questions were largely positive, with 162 out of 168 providing favorable reviews. The multilabel image recognition model evaluation showed that the Dino V2 model achieved an average F1 score of 0.825, indicating high accuracy in recognizing ingredients.
The model evaluations were promising. The AI-based nutritionist program is now ready for a supervised pilot study.
摘要：
背景：由于注册临床营养师供应不足，中国糖尿病患者的营养管理是一项重大挑战。为了解决这个问题，创建了一个基于人工智能（AI）的营养师程序，该程序使用高级语言和图像识别模型。该程序可以从患者的膳食图像中识别成分，并提供营养指导和饮食建议。
目的：本研究的主要目的是评估支持该计划的模型的能力。
方法：通过多步骤过程评估AI营养师计划对2型糖尿病（T2DM）患者的潜力。首先,我们在2型糖尿病患者和内分泌学家中进行了一项调查,以确定饮食习惯方面的知识差距.然后通过中国注册营养师考试对ChatGPT和GPT4.0进行测试，以评估他们提供循证饮食建议的熟练程度。将ChatGPT对有关医学营养治疗的常见问题的回答与专业营养师的专家回答进行比较，以评估其熟练程度。该模型的食品建议经过仔细审查，以确保与专家建议保持一致。开发了基于深度学习的图像识别模型，用于成分级别的食品识别，并将其性能与现有模型进行了比较。最后,开发了一个用户友好的应用程序，整合语言和图像识别模型的功能，以潜在地改善对T2DM患者的护理。
结果：大多数患者（182/206，88.4％）需要更直接和全面的营养管理和教育。ChatGPT和GPT4.0都通过了中国注册营养师考试。ChatGPT的食品建议主要符合最佳实践，除了某些食物，如根茎类蔬菜和干豆。专业营养师对ChatGPT对常见问题的回答的评论在很大程度上是积极的，168人中有162人提供好评。多标签图像识别模型评估表明，DinoV2模型的平均F1得分为0.825，表明识别成分的准确性很高。
结论：模型评估是有希望的。基于AI的营养师计划现在已经准备好进行有监督的试点研究。
目的：本研究的主要目的是评估支持该计划的模型的能力。
方法：通过多步骤过程评估AI营养师计划对2型糖尿病（T2DM）患者的潜力。首先,我们在2型糖尿病患者和内分泌学家中进行了一项调查,以确定饮食习惯方面的知识差距.然后通过中国注册营养师考试对ChatGPT和GPT4.0进行测试，以评估他们提供循证饮食建议的熟练程度。将ChatGPT对有关医学营养治疗的常见问题的回答与专业营养师的专家回答进行比较，以评估其熟练程度。该模型的食品建议经过仔细审查，以确保与专家建议保持一致。开发了基于深度学习的图像识别模型，用于成分级别的食品识别，并将其性能与现有模型进行了比较。最后,开发了一个用户友好的应用程序，整合语言和图像识别模型的功能，以潜在地改善对T2DM患者的护理。
结果：大多数患者（182/206，88.4％）需要更直接和全面的营养管理和教育。ChatGPT和GPT4.0都通过了中国注册营养师考试。ChatGPT的食品建议主要符合最佳实践，除了某些食物，如根茎类蔬菜和干豆。专业营养师对ChatGPT对常见问题的回答的评论在很大程度上是积极的，168人中有162人提供好评。多标签图像识别模型评估表明，DinoV2模型的平均F1得分为0.825，表明识别成分的准确性很高。
结论：模型评估是有希望的。基于AI的营养师计划现在已经准备好进行有监督的试点研究。
