Can AI assist academic researchers in quickly screening literature reviews?

Artificial intelligence can significantly accelerate the literature screening process for academic researchers. AI tools leverage natural language processing and machine learning to efficiently evaluate large volumes of research abstracts and articles for relevance to a specific research question. Effective AI-assisted screening requires high-quality input data, including clearly defined inclusion/exclusion criteria and representative seed articles for training classifiers. These systems typically perform best for well-specified topics within structured literature reviews like systematic reviews or scoping reviews, rapidly identifying potentially relevant papers based on semantic similarity or predefined keywords. Human oversight remains essential to validate relevance, manage nuanced decisions, and minimize false negatives or false positives. Implementation quality depends critically on the AI model's training and the query's clarity. Implementation involves researchers first establishing search strategies and criteria. AI platforms then ingest search results, automatically rank articles by predicted relevance, and often highlight key sections like methods and conclusions for fast manual review. This significantly reduces the initial screening burden, conserving researcher time for critical appraisal and synthesis. Key applications include facilitating comprehensive systematic reviews and expediting grant proposal background research.