How can AI tools be used to enhance the efficiency of literature retrieval?

AI tools substantially enhance literature retrieval efficiency by automating searches and intelligently analyzing vast academic text volumes. Their implementation is both technically feasible and increasingly accessible to researchers. Key principles involve using natural language processing (NLP) for semantic understanding, enabling queries beyond simple keywords. Machine learning algorithms improve relevance ranking and personalize recommendations based on user history. Automated filtering swiftly excludes irrelevant sources, while summarization tools quickly distill article essence. Crucially, researchers must verify AI-curated results against primary sources to ensure accuracy and mitigate algorithmic bias. Practically, researchers implement AI by utilizing specialized databases offering semantic search capabilities, such as Elicit or Semantic Scholar. They construct queries using context-rich phrases enhanced by AI's synonym recognition. Integrating AI-powered reference managers streamlines organizing and deduplicating findings. Ultimately, leveraging AI for trend identification and citation mapping uncovers critical research avenues faster, accelerating the literature review phase and foundational knowledge acquisition.