CDTD-Agent: a retrieval-augmented multi-agent digital health framework for integrated diabetes care: a single-case expert-rated pilot study
Abstract
Background: Diabetes management increasingly requires coordination across Western medicine, traditional Chinese medicine (TCM), and lifestyle-based health management. General-purpose large language models (LLMs) can generate clinical text but may lack reliable source grounding and structured cross-disciplinary reasoning.
Methods: We developed CDTD-Agent, a retrieval-augmented multi-agent framework that combines a domain-specific knowledge base with an Extract-Retrieve-Reason workflow. The knowledge base included 12 medical textbooks, 8 clinical guidelines, and 183 expert-reviewed diabetes cases. In a blinded single-case pilot evaluation, 30 senior experts rated anonymized recommendations from CDTD-Agent, GPT-4o, DeepSeek-V3.2-Exp, Doubao, and two attending physicians across eight dimensions. Linear mixed-effects models accounted for repeated ratings within evaluators, with Holm correction across eight omnibus tests and global Benjamini-Hochberg correction across 40 focused CDTD-Agent-versus-comparator contrasts. A post hoc backbone-only ablation compared the full framework with Qwen3-235B-A22B using the same case and external prompt without retrieval or role-structured multi-agent processing.
Results: Significant source effects were detected in all eight dimensions after Holm correction (all adjusted p p post hoc ablation, the full framework outperformed the backbone-only condition in seven of eight dimensions after Holm correction; health management was not significantly…
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