ArticleJMIR formative research2024
Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.
Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Review
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends.Journal of multidisciplinary healthcare · 2025Article
- Diagnosing Glaucoma Based on the Ocular Hypertension Treatment Study Dataset Using Chat Generative Pre-Trained Transformer as a Large Language Model.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundIn this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs).
objectiveThe aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers.
methodsWe developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease-related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field.
resultsThe classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F
conclusionsThe proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.
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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.