ArticleOphthalmology science2026
Exploration of Morphological Characteristics in Diabetic Macular Ischemia Utilizing a Large Language Model-Assisted Knowledge Graph.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Evaluation of large language model-generated information in diabetes health patient education: a scoping review.Frontiers in public health · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To explore novel clinical terminologies in diabetic macular ischemia (DMI) using a large language model-assisted knowledge graph (KG) constructed from published literature and to validate the findings using clinical data. Design: A review incorporating KG construction and subsequent exploration of clinical terminologies, validated in an observational cohort study. Participants: Sixty-six original and review articles on DMI published between July 2008 and March 2025 were identified through PubMed, MEDLINE, and Embase. Validation was performed using data from 156 eyes of 156 patients with vision-threatening DMI. Methods: Using generative pre-trained transformer 4, article texts were processed into entity-relation triplets. Entities were annotated with 13 predefined clinical properties and assembled into a KG using Neo4j. Community detection via the Leiden algorithm grouped related entities into subgraphs. Interpretation of subgraphs led to 2 novel clinical concepts: Disorganization of MIddle retinal Layers (DMIL), defined as structural disruption between the inner nuclear layer and outer plexiform layer; and degenerative DMI, defined as DMI with retinal neurodegenerative findings. Main Outcome Measures: Characteristics of KG-derived subgraphs and definition of novel clinical terminologies. Results: The final KG contained 2408 entities and 8133 relations. Simplified graphs composed of highly frequent entities revealed structured relationships among important terminologies; for example, disease concepts, imaging modalities, vascular parameters, and visual acuity (VA) in DMI. Community detection showed uneven distribution of entities between subgraphs. Interpretation of subgraphs divided by community detection led to the identification of Conclusions: Large language model-assisted KG construction enables objective synthesis of clinical literature and facilitates the exploration of known and unknown clinical characteristics in DMI. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.