This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (708) Information Retrieval (348) Natural Language Processing (917) Diabetes Education and Elearning for Patients (152) Diabetes Self-Management (588) Artificial Intelligence in Diabetes Care and Prevention (54)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58041, first published .
Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

Authors of this article:

Dingqiao Wang1 Author Orcid Image ;   Jiangbo Liang1 Author Orcid Image ;   Jinguo Ye1 Author Orcid Image ;   Jingni Li1 Author Orcid Image ;   Jingpeng Li1 Author Orcid Image ;   Qikai Zhang1 Author Orcid Image ;   Qiuling Hu1 Author Orcid Image ;   Caineng Pan1 Author Orcid Image ;   Dongliang Wang1 Author Orcid Image ;   Zhong Liu1 Author Orcid Image ;   Wen Shi1 Author Orcid Image ;   Danli Shi2 Author Orcid Image ;   Fei Li1 Author Orcid Image ;   Bo Qu3 Author Orcid Image ;   Yingfeng Zheng1 Author Orcid Image
Article Authors Cited by (26) Tweetations (1) Metrics

Journals

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  2. Liu S, McCoy A, Wright A. Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines. Journal of the American Medical Informatics Association 2025;32(4):605 View
  3. Kelly A, Noctor E, Ryan L, van de Ven P. The Effectiveness of a Custom AI Chatbot for Type 2 Diabetes Mellitus Health Literacy: Development and Evaluation Study. Journal of Medical Internet Research 2025;27:e70131 View
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  6. Sumner J, Wang Y, Tan S, Chew E, Wenjun Yip A. Perspectives and Experiences With Large Language Models in Health Care: Survey Study. Journal of Medical Internet Research 2025;27:e67383 View
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  10. Zhang C, Yang H, Liu X, Wu R, Zong H, Wu E, Zhou Y, Li J, Shen B. A Knowledge-Enhanced Platform (MetaSepsisKnowHub) for Retrieval Augmented Generation–Based Sepsis Heterogeneity and Personalized Management: Development Study. Journal of Medical Internet Research 2025;27:e67201 View
  11. Amugongo L, Mascheroni P, Brooks S, Doering S, Seidel J, Liu X. Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digital Health 2025;4(6):e0000877 View
  12. Fukui Y, Kawata Y, Kobashi K, Nagatani Y, Iguchi H. Evaluation of a retrieval-augmented generation system using a Japanese Institutional Nuclear Medicine Manual and large language model-automated scoring. Radiological Physics and Technology 2025;18(3):861 View
  13. Wu X, Cai G, Guo B, Ma L, Shao S, Yu J, Zheng Y, Wang L, Yang F. A multi-dimensional performance evaluation of large language models in dental implantology: comparison of ChatGPT, DeepSeek, Grok, Gemini and Qwen across diverse clinical scenarios. BMC Oral Health 2025;25(1) View
  14. Zhu X, Dai W, Evans R, Geng X, Mu A, Liu Z. Current Landscape and Future Directions Regarding Generative Large Language Models in Stroke Care: Scoping Review. JMIR Medical Informatics 2025;13:e76636 View
  15. Song J, Xu Z, He M, Feng J, Shen B. Graph retrieval augmented large language models for facial phenotype associated rare genetic disease. npj Digital Medicine 2025;8(1) View
  16. Wang D, Ye J, Li J, Liang J, Zhang Q, Hu Q, Pan C, Wang D, Liu Z, Shi W, Guo M, Li F, Du W, Zheng Y. Enhancing Large Language Models for Improved Accuracy and Safety in Medical Question Answering: Comparative Study. JMIR Medical Education 2025;11:e70190 View
  17. Anghelescu A, Munteanu C, Anghelescu L, Onose G. ”A Midsummer Night’s Dream” quest for truth: From ChatGPT “hallucinations” to RAG reasoning and ACURAI precision — a scoping review on detection, minimizing, and (almost) complete error elimination and enhancing Large Language Models' re-liability. Balneo and PRM Research Journal 2025;16(Vol 16 No. 3):847 View
  18. Jeon S, Lee S, Kim E, Eun J, Lee K, Lim H, Lee J. Generative AI Chatbot for Diabetes Management: Formative 2-Part Qualitative Study Using DTalksBot Involving Patients and Clinicians. JMIR Formative Research 2025;9:e72553 View
  19. Serugunda H, Jianquan O, Kasujja Namatovu H, Ssemaluulu P, Kimbugwe N, Garimoi Orach C, Waiswa P. Using Large Language Models for Chronic Disease Management Tasks: Scoping Review. JMIR Medical Informatics 2025;13:e66905 View
  20. Bayram H, Arslan S, Ozturkcan A. Evaluating AI‐Generated Meal Plans for Simulated Diabetes Profiles: A Guideline‐Based Comparison of Three Language Models. Journal of Evaluation in Clinical Practice 2025;31(7) View
  21. Abo El-Enen M, Saad S, Nazmy T. A survey on retrieval-augmentation generation (RAG) models for healthcare applications. Neural Computing and Applications 2025 View
  22. Mansoor H. A Scoping Review of Large Language Models in Personal Sleep Wellness. Mayo Clinic Proceedings: Digital Health 2025;3(4):100301 View
  23. Tung J, Le Q, Yao J, Huang Y, Lim D, Sng G, Lau R, Tan Y, Chen K, Tay K, Tan J, Yuen J, Cheng C, Ho H. Performance of Retrieval-Augmented Generation Large Language Models in Guideline-Concordant Prostate-Specific Antigen Testing: Comparative Study With Junior Clinicians. Journal of Medical Internet Research 2025;27:e78393 View
  24. Gültekin O, Sezgin E, Cakır O, Sengul H, Kilinc B, Yılmaz B, Kocaoglu B, Kayaalp M. Retrieval‐augmented ChatGPT‐4o improves accuracy but reduces readability in hip arthroscopy patient education. Knee Surgery, Sports Traumatology, Arthroscopy 2025 View

Books/Policy Documents

  1. Siddireddy S, Sahay A, Singh T. Emerging Trends in Artificial Intelligence and Machine Learning. View

Conference Proceedings

  1. Zhong C, Ye F, Wang Z, Jigeer A, Zhan Z. 2025 14th International Conference on Educational and Information Technology (ICEIT). Interdisciplinary-QG: An LLM-Based Framework for Generating High-Quality Interdisciplinary Test Questions with Knowledge Graphs and Chain-of-Thought Reasoning View