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Advancing retrieval-augmented medical AI: methodological considerations for the HEART framework in hypertension education

Hypertension Research (2026) Cite this article

We read with great interest the recent article by Wang et al. entitled “A multi-layer retrieval-augmented large language model framework for enhancing hypertension education” [1]. The authors are to be commended for developing the HEART framework, a thoughtfully designed retrieval-augmented generation (RAG) system that integrates a curated hypertension knowledge base with large language models (LLMs), achieving consistent improvements across accuracy, completeness, consistency, robustness, and safety.

This study addresses a critical challenge in medical artificial intelligence—the reliability and clinical safety of LLM-generated health information [1]. The multi-layer architecture of the HEART framework, including query reformulation, knowledge retrieval, information filtering, and answer generation, represents a structured and clinically meaningful solution. In particular, the integration of expert-curated knowledge sources and the implementation of safety-oriented filtering mechanisms are notable strengths that enhance the trustworthiness of the system. The comprehensive, clinician-based evaluation across multiple quality dimensions further supports the rigor and relevance of the findings.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  1. Wang Y, Luan Y, Cheng S, et al. A multi-layer retrieval-augmented large language model framework for enhancing hypertension education. Hypertens Res. 2026;49:1428–40. https://doi.org/10.1038/s41440-025-02481-9.

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Authors and Affiliations

  1. Translational Clinical Immunology Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, PR China

    Yi Wang

  2. Department of Cardiovascular Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu, PR China

    Shangxuan Li & Dianyuan Li

  3. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, PR China

    Zekai Yu

  4. Department of Pharmacy, Fuzhou Medical College, South Campus of Nanchang University Fuzhou Medical College, Fuzhou, Jiangxi, PR China

    Yiru Zhao

  5. The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, PR China

    Yuhan Zou

Authors
  1. Yi Wang
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  2. Shangxuan Li
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  3. Zekai Yu
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  4. Yiru Zhao
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  5. Yuhan Zou
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  6. Dianyuan Li
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Contributions

Yi Wang: Conceptualization, Formal Analysis, Writing-Review & Editing. Shangxuan Li: Investigation, Formal Analysis, Writing-Original Draft, Writing-Review & Editing. Zekai Yu: Investigation, Formal Analysis, Writing-Review & Editing. Yiru Zhao: Formal Analysis, Writing-Review & Editing. Yuhan Zou: Formal Analysis, Writing-Review & Editing. Dianyuan Li: Conceptualization, Supervision, Funding acquisition, Writing-Review & Editing. The author has read and agreed to the published version of the manuscript.

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Correspondence to Dianyuan Li.

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The authors declare no competing interests.

Use of Generative AI

During the preparation of this work the authors used Gemini 3.1 Pro to improve the readability and language quality of the manuscript. After using this tool the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

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Wang, Y., Li, S., Yu, Z. et al. Advancing retrieval-augmented medical AI: methodological considerations for the HEART framework in hypertension education. Hypertens Res (2026). https://doi.org/10.1038/s41440-026-02677-7

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