The Effectiveness of a Custom AI Chatbot for Type 2 Diabetes Mellitus Health Literacy: Development and Evaluation Study

A RAG-based large language model chatbot can deliver contextually appropriate, empathetic, and clinically credible responses to T2DM queries. By consistently citing trusted sources and notifying users when relying on general knowledge, this approach enhances transparency and trust. The findings have relevance for health educators, highlighting that patient-centric reference documents-structured to address frequent patient questions-are particularly effective. Moreover, instances in which the chatbot signals that it has drawn on general knowledge can provide opportunities for health educators to refine and expand their materials, ensuring that more future queries are answered from trusted sources. The findings suggest that such chatbots may support patient education, promote self-management, and be readily adapted to other health contexts.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-03-25
DOI
https://doi.org/10.2196/70131
Citations
10
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
28.15
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article

The Effectiveness of a Custom AI Chatbot for Type 2 Diabetes Mellitus Health Literacy: Development and Evaluation Study

Lorna Ryan, Pepijn W.J. van de Ven, Eoin Noctor, Anthony Kelly
10 citations
Journal of Medical Internet Research
Mobile Health and mHealth Applications
28.15
article

The Effectiveness of a Custom AI Chatbot for Type 2 Diabetes Mellitus Health Literacy: Development and Evaluation Study

Lorna Ryan, Pepijn W.J. van de Ven, Eoin Noctor, Anthony Kelly
article en
10 citations

Abstract

A RAG-based large language model chatbot can deliver contextually appropriate, empathetic, and clinically credible responses to T2DM queries. By consistently citing trusted sources and notifying users when relying on general knowledge, this approach enhances transparency and trust. The findings have relevance for health educators, highlighting that patient-centric reference documents-structured to address frequent patient questions-are particularly effective. Moreover, instances in which the chatbot signals that it has drawn on general knowledge can provide opportunities for health educators to refine and expand their materials, ensuring that more future queries are answered from trusted sources. The findings suggest that such chatbots may support patient education, promote self-management, and be readily adapted to other health contexts.

Journal of Medical Internet ResearchVol. 27
University of Limerick (IE), University Hospital Limerick (IE)
Quality Education
Openalex Percentile: Top 0%
Mobile Health and mHealth Applications
28.15
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