Introduction and Objective: Adolescents with type 1 diabetes (T1D) require contextually-aware, evidence-based self-management support. General-purpose large language models (LLMs) lack integration with systematically synthesized and appraised evidence or contextual tailoring. This study aimed to construct and evaluate T1DiaBot, a knowledge graph-grounded agent designed for adolescents with T1D. Methods: A self-developed “Evidence-to-Performance” (E2P) framework was followed: 1) Knowledge Graph Construction: The Diabetes Self-Management Knowledge Graph (DSM-KG) was constructed through synthesizing clinical guidelines and reviews, followed by contextual adaptation. 2) Agent Development: T1DiaBot was developed using LangChain, employing a Graph-RAG (Retrieval-Augmented Generation) approach where the DSM-KG serves as the authoritative grounding source. 3) Validation: Clinical validity was assessed via a two-round Delphi experts panel (N=10); usability and safety via a one-month beta-test (N=20 users). 4) Benchmarking: Performance was compared against four general-purpose LLMs (DeepSeek-R1, DeepSeek-V3, Kimi, Doubao) using 81 real-world queries, blindly rated by clinical experts (N=6) and a youth advisory panel (N=27 users). Results: The DSM-KG comprises six sub-graphs (e.g., non-familial setting management, insulin adjustment, parent-child communication, psychosocial support). T1DiaBot achieved high expert consensus (Kendall’s W=0.48-0.63), excellent usability (mean System Usability Scale score: 86.5/100), and no adverse events. It outperformed the four general LLMs in Content Accuracy and Adolescent Scenario Suitability (all P0.001) and showed consistent superiority over one benchmark (Kimi) in all user experience dimensions (p0.05). Conclusion: T1DiaBot is a clinically accurate and culturally tailored AI tool for supporting T1D self-management. The E2P framework provides a rigorous methodology for creating evidence-based AI agents in chronic disease care. Disclosure J. Yang: None. J. Zhang: None. J. Luo: None. W. Guo: None. H. Zhao: None. S. Yu: None. Z. Liao: None. J. Guo: None. Funding The Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grants 2023ZD0508200 and 2023ZD0508204), the National Natural Science Foundation of China (Grant 72264037), and Sinocare Diabetes Foundation (Grant 2024SD05)
YANG et al. (Fri,) studied this question.
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