AI Tools for Literature Reviews: What Researchers Should Use and Verify

How researchers can use AI assistants for search, screening, extraction, synthesis, and citation checking without outsourcing scientific judgement.

AI is useful, but not an author

AI tools can accelerate literature discovery, summarize abstracts, cluster themes, screen records, extract data fields, and surface citation context. They should not decide eligibility, invent references, replace full-text reading, or write conclusions without human verification. The scientific claim remains the author's responsibility.

Where AI helps most

The strongest use cases are repetitive and auditable: generating search synonyms, comparing inclusion criteria, building extraction tables, identifying contradictory citation contexts, and checking whether a reference actually supports a statement.

Verification workflow

Every AI-generated summary should be traced back to the source paper. For systematic reviews, keep the search strategy, screening decisions, exclusion reasons, prompts, extraction fields, and human reviewer decisions. If the workflow cannot be explained to a reviewer, it is not ready for publication.

Policy signal for journals

Journals should allow responsible AI assistance while requiring disclosure when AI materially supports writing, screening, translation, analysis, or figure generation. AI can assist the process, but named authors remain accountable for accuracy, originality, ethics, and references.

Further reading

• Elicit
• Scite
• Semantic Scholar API