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Generative AI Chatbot for Diabetes Management: Formative 2-Part Qualitative Study Using DTalksBot Involving Patients

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Summary
This summary is machine-generated.

Generative AI chatbots offer promising, personalized support for diabetes management, addressing information overload. Future iterations require real-time data integration and clinical workflow alignment for enhanced safety and utility.

Keywords:
diabetes managementdiabetes mellitusgenerative AI chatbotlarge language models (LLMs)online health information seekingretrieval-augmented generation (RAG)

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Diabetes Management

Background:

  • Diabetes mellitus necessitates continuous self-management to prevent complications.
  • Existing online resources and mobile apps for diabetes information often lead to overload, lack personalization, and are difficult to navigate.
  • Generative AI chatbots present a potential solution by offering accessible, personalized, and responsive guidance.

Purpose of the Study:

  • To explore the potential role of generative AI chatbots in diabetes management through a two-part qualitative evaluation.
  • To examine patient information needs, user experiences, and expectations regarding generative AI chatbots.
  • To investigate specialists' perspectives on the practical utility of generative AI chatbots in supporting diabetes self-management and identify appropriate boundaries for their involvement.

Main Methods:

  • Utilized DTalksBot, a generative AI chatbot powered by GPT-4 with retrieval-augmented generation.
  • Part 1 involved 24 patients engaging in structured chatbot sessions, post-interaction surveys, and in-depth interviews, with data analyzed using thematic and content analysis.
  • Part 2 involved 4 family medicine specialists assessing chatbot response accuracy and providing expert insights.

Main Results:

  • Patients submitted 643 questions categorized into personalized health advice, complications, medication, and mental health support, valuing faster access and reduced cognitive burden.
  • Patients found generative AI chatbots more advantageous than traditional sources, offering reliable content and a comfortable space for sensitive topics.
  • Specialists acknowledged the utility of generative AI chatbots for routine inquiries but identified limitations in contextual accuracy, real-time data integration, and personalization.

Conclusions:

  • Generative AI chatbots show potential as complementary tools for diabetes self-management, providing accessible, reliable, and tailored support.
  • This evaluation offers empirical evidence for using generative AI chatbots to meet patient information needs and supplement existing healthcare resources.
  • Future advancements should focus on integrating real-time health data, enhancing contextual relevance, and aligning with clinical workflows to ensure safety, trust, and broad applicability.