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.

提出了一种基于大型语言模型的自动化文章分类方法，主要关注眼科领域，但可扩展到其他领域，采用自然语言处理技术，包括零 - shot 学习的 LLM 模型，通过比较不同变种的 BART，BERT 以及其变种（如 distilBERT，SciBERT，PubmedBERT，BioBERT），证明了 LLM 在无人干预的情况下，对大量眼科论文进行分类的有效性，通过趋势分析使得研究人员和临床医生能够轻松分类和检索相关论文，在文献综述、信息收集、以及不同学科领域内新兴科学趋势识别方面节省时间和精力，同时，该模型在其他科学领域的可扩展性也拓宽了其在促进研究和趋势分析方面的影响。