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Related Experiment Video

Updated: Feb 25, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Exploration of Morphological Characteristics in Diabetic Macular Ischemia Utilizing a Large Language Model-Assisted

Miyo Yoshida1, Tomoaki Murakami1, Kenji Ishihara1

  • 1Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Ophthalmology Science
|February 24, 2026
PubMed
Summary

This study used AI to build a knowledge graph from DMI literature, identifying novel concepts like Disorganization of MIddle retinal Layers (DMIL) and degenerative DMI. These findings were validated in patient data, showing significant impacts on visual acuity.

Keywords:
Diabetic macular edemaDiabetic macular ischemiaOCTOCT angiographyVisual acuity

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Diabetic macular ischemia (DMI) poses a significant threat to vision.
  • Current understanding of DMI terminologies may be incomplete.
  • Novel approaches are needed to synthesize complex clinical information.

Purpose of the Study:

  • To explore novel clinical terminologies in DMI using a large language model-assisted knowledge graph (KG).
  • To construct a KG from published literature on DMI.
  • To validate the KG-derived findings using clinical data.

Main Methods:

  • A systematic literature review identified 66 articles on DMI.
  • Generative pre-trained transformer 4 processed texts into entity-relation triplets.
  • Neo4j was used to assemble a KG, with community detection identifying novel concepts: Disorganization of MIddle retinal Layers (DMIL) and degenerative DMI.

Main Results:

  • The KG contained 2408 entities and 8133 relations, revealing structured relationships in DMI.
  • Novel terminologies DMIL and degenerative DMI were identified.
  • DMIL was associated with significantly worse visual acuity (P < 0.001).
  • Degenerative DMI correlated with capillary nonperfusion and visual acuity reduction (P < 0.001).

Conclusions:

  • Large language model-assisted KG construction objectively synthesizes clinical literature.
  • This approach facilitates the exploration of known and unknown clinical characteristics in DMI.
  • Novel terminologies like DMIL and degenerative DMI offer new insights into DMI pathophysiology and clinical impact.