LLM-powered multimodal AI conversations for diabetes prevention

Simple item page
dc.contributor.authorDao, Dungen
dc.contributor.authorTeo, Jun Yi Claireen
dc.contributor.authorWang, Wenruen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderMinistry of Health -Singaporeen
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-05-29T14:14:42Z
dc.date.available2024-05-29T14:14:42Z
dc.date.issued2024en
dc.description.abstractThe 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.en
dc.description.sponsorshipMinistry of Health -Singapore (Grant No: MOH-000742-00); Science Foundation Ireland (Grant number 12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDao, D., Teo, J. Y. C., Wang, W. and Nguyen, H. D. (2024) 'LLM-powered multimodal AI conversations for diabetes prevention', Proceedings of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia (AIQAM ’24), Phuket, Thailand, 10 June 2024. New York, NY, USA: ACM, pp. 1-6. https://doi.org/10.1145/3643479.3662049 en
dc.identifier.doi10.1145/3643479.3662049en
dc.identifier.endpage6en
dc.identifier.isbn979-8-4007-0547-2/24/06en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15953
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.ispartof1st ACM Workshop on AI-Powered Q&A Systems for Multimedia (AIQAM ’24), Phuket, Thailand, 10 June 2024.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.rights© 2024, the Authors. This work is licensed under a Creative Commons Attribution International 4.0 Licenseen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectDesignen
dc.subjectMultimodalen
dc.subjectConversationalen
dc.subjectGPT-3.5en
dc.subjectDiabetesen
dc.subjectDiabetes preventionen
dc.subjectDialogueen
dc.subjectFine-tuningen
dc.titleLLM-powered multimodal AI conversations for diabetes preventionen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
3643479.3662049.pdf
Size:
1.42 MB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Computer Science - Conference Items
Insight Centre for Data Analytics - Conference Items