Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.

ABSTRACT:

Background

In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs).

Objective

The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers.

Methods

We 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 disea

SUBMITTER: Raja H 

PROVIDER: S-EPMC10998173 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature