  Purpose: In this paper, we present an automated method for article
classification, leveraging the power of Large Language Models (LLM). The
primary focus is on the field of ophthalmology, but the model is extendable to
other fields. Methods: We have developed a model based on Natural Language
Processing (NLP) techniques, including advanced LLMs, to process and analyze
the textual content of scientific papers. Specifically, we have employed
zero-shot learning (ZSL) LLM models and compared against Bidirectional and
Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder
Representations from Transformers (BERT), and its variant such as distilBERT,
SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate
the effectiveness of LLMs in categorizing large number of ophthalmology papers
without human intervention. Results: To evalute the LLMs, we compiled a dataset
(RenD) of 1000 ocular disease-related articles, which were expertly annotated
by a panel of six specialists into 15 distinct categories. The model achieved
mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset.
Conclusion: The 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 in other
domains too. We performed trend analysis that enables the 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.
