Author ORCID Identifier
20th International Conference on Information Systems for Crisis Response and Management (ISCRAM)
Low socioeconomic status (SES) and inadequate nutrition during pregnancy are linked to health disparities and adverse outcomes, including an increased risk of preterm birth, low birth weight, and intrauterine growth restriction. AI-powered computational agents have enormous potential to address this challenge by providing nutrition guidelines or advice to patients with different health literacy and demographics. This paper presents our preliminary exploration of creating a GPT-powered AI chatbot called NutritionBot and investigates the implications for pregnancy nutrition recommendations. We used a user-centered design approach to define the target user persona and collaborated with medical professionals to co-design the chatbot. We integrated our proposed chatbot with ChatGPT to generate pregnancy nutrition recommendations tailored to patients’ lifestyles. Our contributions include introducing a design persona of a pregnant woman from an underserved population, co-designing a nutrition advice chatbot with healthcare experts, and sharing design implications for future GPT-based nutrition chatbots based on our preliminary findings.
Tsai, C. H., Kadire, S., Sreeramdas, T., Vanormer, M., Thoene, M., Hanson, C., Anderson Berry, A. and Khazanchi, D. (2023). Generating Personalized Pregnancy Nutrition Recommendations with GPT-Powered AI Chatbot. In: 20th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2023), 263-271.