LLM-Powered Multimodal AI Conversations for Diabetes Prevention
Authors: 
Dung Dao, Jun Yi Claire Teo, Wenru Wang, and Hoang D. NguyenAuthors Info & Claims
Dung Dao, Jun Yi Claire Teo, Wenru Wang, and Hoang D. NguyenAuthors Info & ClaimsJune 2024
Pages 1 - 6
Abstract
The global prevalence of diabetes remains high despite rising life expectancy with improved quality and access to healthcare services. The significant burden that diabetes imposes warrants efforts to improve existing interventions in diabetes care. Present research on diabetes management has shown that artificial intelligence (AI) and Large Language Models (LLM) play an important role in various aspects of the diabetes continuum but a distinct lack of studies in diabetes prevention is observed. Our research introduces a comprehensive digital solution, leveraging the capabilities of GPT-3.5 models maintained by OpenAI, focused specifically on the active prevention of diabetes. The system encompasses a user-friendly interface accessible via mobile and web applications, an AI-powered chatbot for instant Q&A and advice, personalized reminder systems, a data analysis module for tailored guidance, resource aggregators for health-related information, and an emotional support module to ensure a holistic approach to prevention. Furthermore, our experiments involved testing the quality of responses generated by a fine-tuned GPT-3.5 model, utilizing the Assistants API or a retrieval-augmented generation (RAG) system powered by FAISS for enhanced context awareness and personalized advice. The testing focused on a structured dataset of questions and answers related to diabetes prevention, with results highlighting the superiority of the GPT-3.5 model combined with the Assistants API in providing relevant, detailed, and personalized responses, thus demonstrating its potential as an invaluable tool in the proactive prevention of diabetes.
References
[1]
Mahyar Abbasian, Zhongqi Yang, Elahe Khatibi, Pengfei Zhang, Nitish Nagesh, Iman Azimi, Ramesh Jain, and Amir M Rahmani. 2024. Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients. arXiv preprint arXiv:2402.10153 (2024).
[2]
Manuela Abbate, Sergio Fresneda, Aina Yanez, Ignacio Ricci-Cabello, Aina M Galmes-Panades, Antoni Aguilo, Miquel Bennasar-Veny, PREDIPHONE trial group, C Vidal, J Llobera, et al. 2021. Nurse-led telephone intervention for lifestyle changes on glycaemic control in people with prediabetes: Study protocol for a randomized controlled trial. Journal of advanced nursing 77, 7 (2021), 3204--3217.
[3]
Melinda S Bender, Bruce A Cooper, Elena Flowers, Raymond Ma, and Shoshana Arai. 2018. Filipinos Fit and Trim-A feasible and efficacious DPP-based intervention trial. Contemporary clinical trials communications 12 (2018), 76--84.
[4]
Adam Bohr and Kaveh Memarzadeh. 2020. The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare. Elsevier, 25--60.
[5]
Michael J Cannon, Svetlana Masalovich, Boon Peng Ng, Robin E Soler, Rajai Jabrah, Elizabeth K Ely, and Bryce D Smith. 2020. Retention among participants in the National Diabetes Prevention Program lifestyle change program, 2012--2017. Diabetes Care 43, 9 (2020), 2042--2049.
[6]
Aastha Chawla, Rajeev Chawla, and Shalini Jaggi. 2016. Microvasular and macrovascular complications in diabetes mellitus: Distinct or continuum? Indian journal of endocrinology and metabolism 20, 4 (2016), 546--551.
[7]
GBD 2019 Ageing Collaborators et al. 2022. Global, regional, and national burden of diseases and injuries for adults 70 years and older: systematic analysis for the Global Burden of Disease 2019 Study. bmj 376 (2022).
[8]
Ivan Contreras and Josep Vehi. 2018. Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research 20, 5 (2018), e10775.
[9]
C. Dayan and J. Platts. 2020. C13.9.1 Diabetes. Oxford University Press, 0.
[10]
Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024. The faiss library. arXiv preprint arXiv:2401.08281 (2024).
[11]
Estelle Everett, Brian Kane, Ashley Yoo, Adrian Dobs, and Nestoras Mathioudakis. 2018. A novel approach for fully automated, personalized health coaching for adults with prediabetes: pilot clinical trial. Journal of medical Internet research 20, 2 (2018), e72.
[12]
Guy Fagherazzi and Philippe Ravaud. 2019. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes & metabolism 45, 4 (2019), 322--329.
[13]
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama 316, 22 (2016), 2402--2410.
[14]
Thierry Hamon and Rémi Gagnayre. 2013. Improving knowledge of patient skills thanks to automatic analysis of online discussions. Patient Education and Counseling 92, 2 (2013), 197--204.
[15]
Dean Ho, Stephen R Quake, Edward RB McCabe, Wee Joo Chng, Edward K Chow, Xianting Ding, Bruce D Gelb, Geoffrey S Ginsburg, Jason Hassenstab, Chih-Ming Ho, et al. 2020. Enabling technologies for personalized and precision medicine. Trends in biotechnology 38, 5 (2020), 497--518.
[16]
Tai Tan Mai, Quang-Linh Tran, Ly-Duyen Tran, Tu Ninh, Duc-Tien Dang-Nguyen, and Cathal Gurrin. 2024. The First ACM Workshop on AI-Powered Question Answering Systems for Multimedia. Proceedings of the 2024 International Conference on Multimedia Retrieval (ICMR'24). https://doi.org/10.1145/3652583.3658890
[17]
John I Malone and Barbara C Hansen. 2019. Does obesity cause type 2 diabetes mellitus (T2DM)? Or is it the opposite? Pediatric diabetes 20, 1 (2019), 5--9.
[18]
Iciar Martín-Timón, Cristina Sevillano-Collantes, Amparo Segura-Galindo, and Francisco Javier del Cañizo-Gómez. 2014. Type 2 diabetes and cardiovascular disease: have all risk factors the same strength? World journal of diabetes 5, 4 (2014), 444.
[19]
Bertalan Meskó et al. 2022. COVID-19's impact on digital health adoption: the growing gap between a technological and a cultural transformation. JMIR Human Factors 9, 3 (2022), e38926.
[20]
Kanyin Liane Ong, Lauryn K Stafford, Susan A McLaughlin, Edward J Boyko, Stein Emil Vollset, Amanda E Smith, Bronte E Dalton, Joe Duprey, Jessica A Cruz, Hailey Hagins, et al. 2023. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet 402, 10397 (2023), 203--234.
[21]
OpenAI. 2023. Assistants API Documentation. https://platform.openai.com/docs/assistants/overview. Accessed: 2024-03-30.
[22]
Dana-Mihaela Petroşanu, Alexandru Pîrjan, and Alexandru Tăbuşcă. 2023. Tracing the Influence of Large Language Models across the Most Impactful Scientific Works. Electronics 12, 24 (2023), 4957.
[23]
Bin Sheng, Zhouyu Guan, Lee-Ling Lim, Zehua Jiang, Nestoras Mathioudakis, Jiajia Li, Ruhan Liu, Yuqian Bao, Yong Mong Bee, Ya-Xing Wang, et al. 2024. Large language models for diabetes care: Potentials and prospects. Science Bulletin (2024), S2095-9273.
[24]
Tayebeh Shirvani, Zeinab Javadivala, Somayeh Azimi, Abdolreza Shaghaghi, Zahra Fathifar, HDR Devender Bhalla, Mohammadhiwa Abdekhoda, and Haidar Nadrian. 2021. Community-based educational interventions for prevention of type II diabetes: a global systematic review and meta-analysis. Systematic reviews 10 (2021), 1--12.
[25]
Sanjana Singareddy, Vijay Prabhu Sn, Arturo P Jaramillo, Mohamed Yasir, Nandhini Iyer, Sally Hussein, and Tuheen Sankar Nath. 2023. Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus 15, 9 (2023).
[26]
Gerald Gui Ren Sng, Joshua Yi Min Tung, Daniel Yan Zheng Lim, and Yong Mong Bee. 2023. Potential and pitfalls of ChatGPT and natural-language artificial intelligence models for diabetes education. Diabetes Care 46, 5 (2023), e103-e105.
[27]
Murugan Subramanian, Anne Wojtusciszyn, Lucie Favre, Sabri Boughorbel, Jingxuan Shan, Khaled B Letaief, Nelly Pitteloud, and Lotfi Chouchane. 2020. Precision medicine in the era of artificial intelligence: implications in chronic disease management. Journal of translational medicine 18 (2020), 1--12.
[28]
Hong Sun, Pouya Saeedi, Suvi Karuranga, Moritz Pinkepank, Katherine Ogurtsova, Bruce B Duncan, Caroline Stein, Abdul Basit, Juliana CN Chan, Jean Claude Mbanya, et al. 2022. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes research and clinical practice 183 (2022), 109119.
[29]
Ting Sun, Yang Xu, Hui Xie, Zuchang Ma, and Yu Wang. 2021. Intelligent personalized exercise prescription based on an ehealth promotion system to improve health outcomes of middle-aged and older adult community dwellers: pretest-posttest study. Journal of Medical Internet Research 23, 5 (2021), e28221.
[30]
Ken Wei Tan, Borame Sue Lee Dickens, and Alex R Cook. 2020. Projected burden of type 2 diabetes mellitus-related complications in Singapore until 2050: a Bayesian evidence synthesis. BMJ Open Diabetes Research and Care 8, 1 (2020), e000928.
[31]
Maria F Vasiloglou, Stavroula Mougiakakou, Emilie Aubry, Anika Bokelmann, Rita Fricker, Filomena Gomes, Cathrin Guntermann, Alexa Meyer, Diana Studerus, and Zeno Stanga. 2018. A comparative study on carbohydrate estimation: GoCARB vs. Dietitians. Nutrients 10, 6 (2018), 741.
[32]
Sanh Victor, Webson Albert, Raffel Colin, Bach Stephen, Sutawika Lintang, Alyafeai Zaid, Chaffin Antoine, Stiegler Arnaud, Raja Arun, Dey Manan, et al. 2022. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations.
[33]
Hao Yang, Jiaxi Li, Siru Liu, Lei Du, Xiali Liu, Yong Huang, Qingke Shi, and Jialin Liu. 2023. Exploring the potential of large language models in personalized diabetes treatment strategies. medRxiv (2023), 2023--06.
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Published In
June 2024
47 pages
ISBN:9798400705472
DOI:10.1145/3643479
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
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Published: 10 June 2024
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- Science Foundation Ireland Centre for Research Training in Artificial Intelligence
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- National Medical Research Council
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