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Diabetes Mellitus: Type 2 and Gestational01:22

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
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GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept.

Edmund Evangelista1, Fathima Ruba2, Salman Bukhari3

  • 1College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates, 971 25993761.

JMIR Diabetes
|January 6, 2026
PubMed
Summary

A novel Graph retrieval-augmented generation (GraphRAG) AI tool enhances gestational diabetes mellitus (GDM) management. This AI improves clinical decision support, offering accurate, evidence-based recommendations for better patient care, especially in underserved areas.

Keywords:
GDMartificial intelligenceartificial intelligence for health careexplainable AI in medicinegenerative AIgestational diabetes mellitusknowledge graphlarge language modelretrieval augmented generation

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Health Informatics

Background:

  • Gestational diabetes mellitus (GDM) is a growing global health concern, particularly affecting underserved populations.
  • Existing generative AI and large language models (LLMs) show potential in healthcare but are underutilized for GDM management.

Purpose of the Study:

  • To evaluate if retrieval-augmented generation (RAG) techniques combined with knowledge graphs (KGs) can enhance AI-driven clinical decision support accuracy and relevance.
  • To develop and validate a GraphRAG-enabled local LLM for GDM management, comparing its performance against other LLM tools.

Main Methods:

  • A GraphRAG prototype was built using 1212 GDM intervention articles (2000-2024) from Semantic Scholar API.
  • The prototype incorporated entity extraction, Neo4j KG construction, and RAG for response generation.
  • Performance was assessed in a simulated environment using clinical and layperson prompts, compared against ChatGPT, Claude, and BioMistral using 5 NLG metrics.

Main Results:

  • The GraphRAG-enabled LLM demonstrated superior accuracy in generating clinically relevant responses.
  • It achieved high scores in Bilingual Evaluation Understudy (0.99), Jaccard similarity (0.98), and BERTScore (0.98), outperforming benchmark LLMs.
  • The prototype provided accurate, evidence-based recommendations, proving its feasibility as a clinical support tool.

Conclusions:

  • GraphRAG-enabled local LLMs offer significant potential for personalized GDM care through integrated evidence and contextual retrieval.
  • The local LLM architecture provides access to advanced medical research for practitioners in underserved regions.
  • KG schema development on peer-reviewed publications ensures accuracy, minimizes hallucinations, and allows patient data contextualization, advancing equitable healthcare delivery.