Challenges of diet planning for children using artificial intelligence

Abstract

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.

Results: 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 (P < 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).

Conclusions: 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.

Keywords: Children; artificial intelligence; diet planning; dieticians.

Fig. 1. Data transformation (A) and restoration process (B) for diet planning with the generative adversarial network.

G, generator model; D, discriminator model.

Fig. 2. Development process of the reinforcement learning-based artificial intelligence solution.

Fig. 3. Proportion of responses to diets designed by humans, RL, and by a GAN. Though the provided diets were the same, the results were different between the first and second surveys. Study experts evaluated the overall evaluations (A), adequacy of nutrients (B), and composition styles of the foods (C).

RL, reinforcement learning; GAN, generative adversarial network; AI, artificial intelligence.