How can I use AI to help me enhance the comprehensiveness of my literature review?

AI can significantly augment literature review comprehensiveness by identifying relevant literature more efficiently and uncovering patterns across vast datasets. It enables researchers to systematically analyze larger volumes of scholarly material than possible manually. Key approaches include leveraging AI-powered research databases (e.g., Semantic Scholar, Elicit) and generative AI tools (like GPT-4 or Claude) for targeted searches and summarization. Successful implementation requires formulating precise search prompts with specific keywords and Boolean operators. AI tools excel at discovering related articles and analyzing citation networks but carry inherent limitations. Crucially, outputs must be rigorously verified against original sources to mitigate risks of hallucinations or factual errors. Human oversight remains essential for interpreting complex nuances and ensuring results align with the review's conceptual framework. Practical steps involve: first, defining clear research questions and inclusion/exclusion criteria; second, utilizing AI search assistants to identify core papers and discover latent connections; third, employing summarization tools to extract key findings; finally, manually reviewing significant publications to verify accuracy and contextualize findings. This approach streamlines coverage across disciplines and identifies seminal and contemporary works, ultimately strengthening the review's foundation and academic rigor.