AI tools for literature reviews: build a workflow you can verify

An AI tool can return a polished evidence table before a research team has established whether the cited papers exist, whether the right participants were studied or whether the table used the correct follow-up. Start with the decision you need help making and the evidence needed to check it. This guide is for researchers and supervisors building a review workflow that another person can inspect, including teams working across Arabic and English.

1. Choose a task before choosing a tool

Separate discovery, screening, extraction and writing. A discovery service may suggest papers; a screening classifier may prioritise records; a document assistant may propose table entries. Success in one task does not establish performance in another. Write a short requirement for each: inputs allowed, output expected, person checking it and the consequence of a wrong answer.

Cochrane’s guidance asks researchers to assess purpose, validation, usability and transparency, while retaining responsibility and human oversight. Use those questions to compare tools available to your team. A useful trial is one in which you can inspect mistakes and export decisions. A fluent demonstration or a vendor’s overall accuracy score does not answer whether the tool is suitable for your review.

Reference: Cochrane: assessing AI tools for evidence synthesis

2. Keep search suggestions separate from searches actually run

Ask a language tool for candidate synonyms, spelling variants or terms that need investigation. Then check those suggestions in the database’s vocabulary and search interface. Keep the question, inclusion criteria and planned sources visible. An attractive list of references returned in a conversation is not an execution record of a database search.

For PubMed, inspect Search Details to see how the query was interpreted. Save the final query and export the results you actually retrieved. If an AI tool supplies a reference, open the publisher or bibliographic record and match title, authors, year and identifier before treating it as a candidate source. A working DOI link can still lead to a different paper.

Reference: NLM: PubMed search, history and export help

3. Calibrate screening on examples that challenge the rules

Before using suggestions in a live review, prepare a practice set with clearly eligible studies, clear exclusions and ambiguous abstracts. Include the languages, study designs and document types the review expects. Have qualified reviewers establish the reference decisions and reasons. Keep this set separate from examples used to adjust the prompt so that the final check is not simply a rehearsal.

Review missed eligible records as well as agreement. Decide beforehand what error would stop use and who resolves uncertainty. For the workflow proposed here, AI suggestions do not autonomously exclude records. If a team plans a different automation role, it needs a justified method and transparent reporting. PRISMA 2020 asks authors to describe screening personnel, independence and how automation was used and validated.

Reference: PRISMA 2020: expanded reporting checklist, items 8–9

4. Audit the value, denominator and time point together

An extraction row should lead back to the exact report, page or table and the supporting text. Include the population, group, outcome definition, time point, value and units. Mark an absent field as not reported or unresolved. A model’s confidence label is not evidence that a number appears in the paper.

The following is an invented training exercise, with no real study or treatment claim. Its source card states: 80 participants enrolled; 64 completed the six-month assessment; 16 of those 64 met the stated outcome. No twelve-month outcome or between-group comparison is supplied. The errors are deliberately constructed; they are not measured performance of an AI product.

On small screens, scroll the table horizontally, or focus it and use the arrow keys.

Fictional extraction audit: check the whole field
Proposed entry	Check against the supplied card	Verified handling
16/80 at six months	The reported outcome denominator is 64 assessed participants	Record 16/64, or 25%, for assessed participants; retain 80 enrolled separately
25% at twelve months	Only a six-month assessment is supplied	Correct the time point; twelve-month outcome remains unavailable
The intervention improved outcomes	No comparison or analysis supports an effect claim	Remove the claim; report the descriptive result with its population
No adverse events	The card contains no adverse-event information	Record not reported in the supplied source; do not enter zero

5. Draft from checked evidence, then read the actual sources

Use the verified extraction sheet to organise a draft, keeping each substantive sentence linked to the relevant source. Read the surrounding passage before accepting a paraphrase. Check negation, uncertainty and whether the statement describes an association, a comparison or a proposed explanation. An Arabic summary should preserve those distinctions even when natural phrasing requires a different sentence structure.

ICMJE makes authors responsible for checking AI-assisted material and requires disclosure of relevant use; AI tools do not qualify as authors. Its guidance also rejects AI-generated material as a primary source. Follow the target journal’s instructions and cite the underlying research for scientific claims. Language assistance does not transfer responsibility for the analysis or interpretation.

Reference: ICMJE: use of AI by authors

6. Keep a compact record that survives a change of tool

For every material AI task, retain the tool and model/version if available, date, purpose, permitted source set, prompt or configuration, output, reviewer decision and unresolved issue. If the provider does not expose a model version, record that limitation. Store confidential material only in a location and service approved for that material; check the actual account’s processing and retention terms before upload.

Use stable local source IDs so exported decisions remain understandable after a subscription ends. Keep raw suggestions distinct from accepted entries. A shared folder of screenshots is harder to audit than a table connecting each suggestion to its source and final decision. The proposed log is a practical team method; it is not a certificate of methodological quality.

7. Prepare the review for a colleague to examine

Give a colleague a source paper, its extraction row and a draft paragraph. They should be able to reproduce the chain without asking the model to explain itself. Resolve broken links, unmatched citations and unanswered eligibility questions before they spread into the manuscript. Keep disagreements visible until the designated reviewer resolves them.

For the search record, PRISMA-S supports reporting sources and platforms, complete strategies, dates, restrictions and management of records. Use the relevant reporting guidance throughout the project. Completing a checklist describes what was reported; it does not by itself establish that the review’s methods or conclusions are sound.

Reference: PRISMA-S: reporting literature searches

Questions for a practical final check

The tool has a defined task and a named reviewer.
Candidate references were matched to actual records.
Extraction preserves denominators, units and time points.
Missing information remains missing, with questions recorded.
Arabic and English claims preserve the same uncertainty.
The methods and AI disclosure describe actual work.

Examples illustrate a working method; they do not establish the readiness of a particular journal or manuscript. This is general guidance: check the current policies of the journal concerned. Source links reviewed 14 September 2026.