TY - GEN
T1 - Generating Personalized Pregnancy Nutrition Recommendations with GPT-Powered AI Chatbot
AU - Tsai, Chun Hua
AU - Kadire, Sathvik
AU - Sreeramdas, Tejesvi
AU - VanOrmer, Matthew
AU - Thoene, Melissa
AU - Hanson, Corrine
AU - Berry, Ann Anderson
AU - Khazanchi, Deepak
N1 - Publisher Copyright:
© 2023 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85171754630
T3 - Proceedings of the International ISCRAM Conference
SP - 263
EP - 271
BT - Proceedings - 20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023
PB - Information Systems for Crisis Response and Management, ISCRAM
T2 - 20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023
Y2 - 28 May 2023 through 31 May 2023
ER -