How can AI tools be used to enhance the depth of the literature review section?

AI tools significantly deepen literature reviews by automating systematic searches, identifying complex thematic connections across vast literature, and uncovering latent research gaps. This enhances analytical rigor and scope beyond manual capabilities. Effective use necessitates selecting domain-appropriate tools like semantic search engines (e.g., Elicit, Semantic Scholar), AI-driven systematic review assistants, and knowledge mapping software. Critical cross-verification of AI-generated insights against original sources is mandatory to ensure accuracy and avoid hallucinations. Setting precise search parameters, utilizing tools for trend analysis, and bias identification are fundamental. Human oversight remains essential for contextual interpretation and quality control. Implementation involves: first defining the review scope; second, deploying AI for comprehensive literature discovery and preliminary thematic clustering; third, utilizing text mining for bias and gap analysis; finally, integrating AI outputs with critical human synthesis. This approach saves time, reveals non-obvious patterns, strengthens argumentation, and ensures a more exhaustive examination of the field, directly elevating the section's scholarly contribution.