A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening.

ABSTRACT: When conducting a systematic review, screening the vast body of literature to identify the small set of relevant studies is a labour-intensive and error-prone process. Although there is an increasing number of fully automated tools for screening, their performance is suboptimal and varies substantially across review topic areas. Many of these tools are only trained on small datasets, and most are not tested on a wide range of review topic areas. This study presents two systematic review datasets compiled from more than 8600 systematic reviews and more than 540000 abstracts covering 51 research topic areas in health and medical research. These datasets are the largest of their kinds to date. We demonstrate their utility in training and evaluating language models for title and abstract scree

SUBMITTER: Chan GCK 

PROVIDER: S-EPMC12527522 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature