Strategizing AI utilization for psychological literature screening: A comparative analysis of machine learning algorithms and key factors to consider

Abstract With the rapid growth of scholarly literature, efficient artificial intelligence (AI)–aided abstract screening tools are becoming increasingly important. This study evaluated 10 different machine learning (ML) algorithms used in AI-aided screening tools for ordering abstracts according to their estimated relevance. We focused on assessing their performance in terms of the number of abstracts required to screen to achieve a sufficient detection rate of relevant articles. Our evaluation included articles screened with diverse inclusion and exclusion criteria. Crucially, we examined how characteristics of the screening data—such as the proportion of relevant articles, the overall frequency of abstracts, and the amount of training data—impacted algorithm effectiveness. Our findings provide valuable insights for researchers across disciplines, highlighting key factors to consider when selecting an ML algorithm and determining a stopping point for AI-aided screening. Specifically, we observed that the algorithm combining the logistic regression (LR) classifier with the sentence-bidirectional encoder representations from transformers (SBERT) feature extractor outperformed other algorithms, demonstrating both the highest efficiency and the lowest variability in performance. Nonetheless, the algorithm’s performance varied across experimental conditions. Building on these findings, we discuss the results and provide practical recommendations to assist users in the AI-aided screening process.