Lee Changhun, Kim Soohyeok, Kim Jayun, Lim Chiehyeon, Jung Minyoung
Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea.
Kosin Innovative Smart Healthcare Research Center, Kosin University Gospel Hospital, Busan 49267, Korea.
Nutr Res Pract. 2022 Dec;16(6):801-812. doi: 10.4162/nrp.2022.16.6.801. Epub 2022 May 23.
BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.
MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps.
In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets ( < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information).
To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.
背景/目的：由于需要营养和发育方面的知识，以及与大量食物相关的高设计复杂性，儿童保育中心的饮食规划很困难。人工智能有望通过自动有效地应用专业知识来提供饮食规划解决方案，解决最佳饮食设计的复杂性问题。本研究展示了对人工智能生成的儿童饮食效用评估的结果，并提供了相关启示。
材料/方法：我们使用生成对抗网络（GAN）模型和强化学习（RL）框架为3至5岁的儿童开发了两种人工智能解决方案。在训练这些解决方案以生成每日饮食计划后，专家分两步对人工生成和人工智能生成的饮食进行了评估。
在营养充足性评估中，当仅向专家提供营养信息而不提供食物名称时，对强化学习生成的饮食的强烈肯定回应比例高于人工生成和生成对抗网络生成的饮食（<0.001）。相比之下，在饮食组成方面，当向专家提供食物名称信息（即组成信息）时，专家对人工设计饮食的回应更为积极。
据我们所知，这是第一项展示人工智能开发及评估以支持儿童饮食规划的研究。本研究证明了开发儿童人工智能辅助饮食规划方法的可能性，并强调了饮食规划中成分合规性的重要性。需要营养、工程和医学领域进一步的综合合作，以提高我们提出的人工智能解决方案的适用性，并通过在成分和营养标准方面提供高质量的饮食规划来促进儿童的健康。
