Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study.

ABSTRACT:

Background

The systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts. The accuracy and efficiency of this process are critical for the quality of the review and subsequent health care decisions. Traditional methods rely heavily on human reviewers, often requiring a significant investment of time and resources.

Objective

This study aims to assess the performance of the OpenAI generative pretrained transformer (GPT) and GPT-4 application programming interfaces (APIs) in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review data sets and comparing their performance against ground truth labeling by 2 independent human revie

SUBMITTER: Guo E 

PROVIDER: S-EPMC10818236 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature