{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Generative AI Chatbot for Diabetes Management: Formative 2-Part Qualitative Study Using DTalksBot Involving Patients and Clinicians.","author":[{"@type":"Person","name":"Soyun Jeon"},{"@type":"Person","name":"Seolhee Lee"},{"@type":"Person","name":"E. Kim"},{"@type":"Person","name":"Jinsu Eun"},{"@type":"Person","name":"Kwangwon Lee"},{"@type":"Person","name":"Hajin Lim"},{"@type":"Person","name":"Joonhwan Lee"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"JMIR Formative Research"},"identifier":"https://doi.org/10.2196/72553","description":"\n Background\n Diabetes mellitus requires continuous self-management to prevent complications. Patients frequently rely on online resources and mobile apps for diabetes-related information; however, these often lead to information overload, limited personalization, and difficulty in navigation. Generative artificial intelligence (AI) chatbots may address these challenges by providing accessible, personalized, and responsive guidance.\n \n \n Objective\n This study aimed to explore the potential role of generative AI chatbots in diabetes management through a 2-part qualitative evaluation. Part 1 examined patients’ information needs, user experiences, and expectations. Part 2 investigated specialists’ perspectives on the practical utility of generative AI chatbots in supporting diabetes self-management. By incorporating perspectives from both patients and specialists, the study aimed to identify appropriate boundaries for the involvement of generative AI chatbots, reflecting the needs and expectations of both stakeholder groups.\n \n \n Methods\n This study was conducted using DTalksBot, a generative AI chatbot powered by GPT-4 (OpenAI) and enhanced with retrieval-augmented generation. In Part 1, we aimed to understand the experiences, needs, and expectations of patients with diabetes. To achieve this, 24 participants engaged in structured chatbot sessions, completed postinteraction surveys, and participated in in-depth interviews. Data were analyzed using thematic and content analysis to identify patterns in user queries and experiences. In part 2, we invited 4 family medicine specialists to assess the accuracy of DTalksBot’s responses by reviewing conversation logs and to share expert insights on the future role of generative AI chatbots in diabetes management.\n \n \n Results\n In part 1, a total of 24 patients submitted a total of 643 questions, which were categorized into 4 primary themes: personalized health advice and guidance (n=281, 44.6%), complications and comorbidities (n=174, 27.1%), medication and treatment exploration (n=111, 17.3%), and mental health management and support (n=30, 4.7%). Patients emphasized the advantages of generative AI chatbots over traditional information sources, including faster access to reliable content, reduced cognitive burden, and the ability to comfortably discuss sensitive topics. In part 2, specialists recognized the generative AI chatbots’ value in answering routine inquiries, but noted limitations in contextual accuracy, real-time data integration, and response personalization.\n \n \n Conclusions\n Generative AI chatbots showed promise as complementary tools for diabetes self-management by offering accessible, reliable, and tailored support. This formative evaluation provides empirical evidence on how generative AI chatbots can address patient information needs and complement existing health care resources. To maximize utility, future generative AI chatbots need to integrate real-time health data, enhance contextual relevance, and align with clinical workflows to ensure safety, trust, and broader applicability.\n ","url":"https://innscience.ca/en/community/literature/97f5bd1d-244c-4676-87b4-c63c21572448"}