Can AI tools assist researchers in conducting batch literature screening?

Yes, AI tools can effectively assist researchers in conducting batch literature screening. They offer significant potential for automating the initial stages of processing large volumes of research publications. These tools primarily leverage natural language processing (NLP) techniques to analyze titles, abstracts, and sometimes full texts, automatically applying predetermined inclusion/exclusion criteria. Successful implementation requires clear screening protocols, high-quality training data, and appropriate algorithm selection. While highly efficient for preliminary filtering large datasets, their accuracy varies based on model complexity and data characteristics. Crucially, they necessitate rigorous validation against human screening and should be viewed as augmentative tools rather than replacements, particularly for nuanced decisions and assessing methodological quality. AI-assisted batch screening significantly accelerates systematic reviews and large-scale evidence synthesis, enhancing research workflow efficiency. Key applications involve rapidly identifying relevant studies from extensive databases, reducing manual screening burden, and allowing researchers to focus resources on full-text assessment and data extraction. This approach facilitates more comprehensive literature coverage and can improve consistency, though final eligibility verification remains essential.