Personalized Meal Planning in Inpatient Clinical Dietetics Using Generative Artificial Intelligence: System Description

Abstract

This study addresses the limitations of traditional prescribed meal plans, which lack personalization and often prove monotonous and challenging for patients to follow. We propose a novel approach employing generative artificial intelligence in the context of a learning health system, with an emphasis on inpatient clinical dietetics. The system incorporates two key models: the Meal Plan Generation Model, MeaIGM, and the Meal Plan Image Generation Model, MealImageGM, leveraging state-of-the-art large language models. Patient information from electronic health records and clinical dietetics guidelines are incorporated into prompts for MeaIGM, which is refined through nutritionist validations and users' feedback. On the other hand, MealImageGM generates visual representations of meal plans to enhance patient engagement, utilizing crowd-sourced feedback to optimize image generation prompts. The overall system process includes extracting data from electronic health records, pre-designed user meal generation prompts, and the generation of personalized meal plans and images. Nutritionists play a crucial role in monitoring patient adherence and preferences, contributing to a continuous learning health system cycle. The proposed framework ensures clinically appropriate and personalized meal plans, aligning with dynamic dietary recommendations. The study emphasizes the importance of patient-physician co-creation for constant optimization and highlights the potential positive impact on health outcomes.