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Do not treat an AI-generated literature review as evidence. Treat it as a draft of claims and candidate references. Validation means tracing every material statement to an original, retrievable source; confirming that each citation exists and its metadata are accurate; reading enough of the source to test whether it supports the exact wording; reproducing the search; documenting AI use; and obtaining qualified human approval before publication or decision-making.
A plausible-looking reference can be fabricated, merged from several papers, incomplete, retracted, or attached to a source that supports only a weaker claim. Citation existence, claim support, search completeness, methodological quality, privacy, and disclosure are separate checks. Passing one does not establish the others.
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Why AI literature reviews require an audit
Large language models generate fluent text by predicting likely sequences, not by guaranteeing that every statement is true or every reference exists. They may invent a title, combine the authors and year of different papers, assign a real DOI to the wrong article, or summarize a real study with the wrong population or result. Even when a citation is genuine, it may not entail the sentence beside it.
The practical risk is not limited to an obviously false paper. A review can contain a real source, a real number, and a confident conclusion while quietly changing an association into a causal claim, omitting uncertainty, or generalizing from a narrow sample to a different population. Validation therefore has to examine the evidence chain, not just the bibliography.
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| Validation question | What a pass establishes | What it does not establish |
|---|---|---|
| Does the work exist? | The title, authors, venue, date and identifier resolve to a real record. | That the source supports the sentence or remains methodologically sound. |
| Does the source support the claim? | The cited passage supports the stated population, method, result and direction. | That the study is unbiased, current or applicable elsewhere. |
| Was the search comprehensive? | Another reviewer can reproduce the documented databases, queries and dates. | That every relevant study was found or that the synthesis is correct. |
| Are methods and judgments defensible? | Eligibility, extraction, risk-of-bias and certainty decisions are recorded and justified. | That an AI system made those decisions reliably. |
| Was AI use transparent and lawful? | Readers can see what tool, stage and controls were used, with confidentiality protected. | That the output is accurate without human review. |
Step 1: Inventory every claim and citation
Split prose into atomic claims
Copy the complete AI output into a working document and break each sentence into the smallest statements that could be independently checked. A sentence saying that an intervention “reduced mortality by 20% in older adults” contains at least a population claim, an intervention claim, an outcome claim, a number and an implied comparison. Record each separately. Do the same for claims in tables, captions, footnotes, headings and generated summaries.
Mark every item that originated with the model: factual assertions, interpretations, quotations, numbers, equations, URLs, DOIs, author names and recommendations. Do not assume that an uncited sentence is harmless; decide whether it needs a source, a qualification or removal.
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| Field | Entry to record |
|---|---|
| Claim ID | A stable identifier such as C-014, retained through revisions. |
| Exact wording | The sentence, table cell or quotation as written, before paraphrasing. |
| Claim type | Background fact, numerical result, causal interpretation, method, limitation or recommendation. |
| Candidate source | Full citation, DOI or URL supplied by the model, plus any replacement source. |
| AI origin | Whether the claim, citation or quotation was generated or merely reformatted. |
| Verification status | Supported, partially supported, unsupported, contradicted, unverifiable or pending. |
| Reviewer note | Page, section, table or passage checked and the correction required. |
This ledger is the audit trail. Keep the original AI wording alongside the corrected version so an editor can see what changed and why. A reference list without claim-level mapping cannot show whether the important conclusions are actually supported.
Step 2: Authenticate every reference
Search authoritative records
For each citation, search the exact title, author list, venue, publication year, DOI and URL in the publisher’s record, Crossref or an appropriate disciplinary database. Oxford Libraries advises checking direct links and DOIs because AI systems can fabricate, combine or incompletely describe references. Confirm that the landing page resolves and that the metadata match the work you intend to cite.
- Copy the title in quotation marks and search it with the first author.
- Check the journal or conference record, not only a secondary citation or an automatically generated result.
- Compare every author, year, volume, issue, page range or article number and DOI.
- Open the DOI and publisher page; record the canonical bibliographic details and access date.
- Check for a correction, expression of concern or retraction in the publisher record and relevant indexing service.
- Label preprints, protocols, conference abstracts and non-peer-reviewed reports accurately instead of presenting them as completed peer-reviewed studies.
Resolve common metadata failures
| Finding | Action |
|---|---|
| No record exists after searches by title, author and venue | Remove the citation or label it unverifiable; never repair it by guessing a similar paper. |
| A DOI resolves to a different title or author list | Mark the citation incorrect and replace it only after locating the correct primary record. |
| Two real papers appear merged | Separate them, assign the right result to each source and recheck every sentence that used the merged citation. |
| The work is retracted or materially corrected | Do not use it as ordinary evidence; explain the status if it is necessary to discuss the history of the finding. |
| Only a secondary summary is available | Use it for orientation, then locate the original study or state that primary verification was not possible. |
Do not ask an AI system to generate or “clean up” the final reference list. JAMA’s author instructions say AI, LLMs and chatbots should not be used to generate or format references because of accuracy and integrity problems. A standard reference manager can format records after a human has verified them.
Step 3: Read the original evidence
A title and abstract are useful triage tools, not proof that a sentence is accurate. Read the full paper whenever the claim affects a conclusion, policy, clinical decision or quantitative synthesis. At minimum, compare the AI summary with the abstract, methods, results and limitations.
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| Element to compare | Questions for the source |
|---|---|
| Design | Is it randomized, observational, diagnostic, qualitative, modelling, a review or a protocol? |
| Population and setting | Who was studied, where and when? Do the participants match the claim? |
| Intervention or exposure | What was actually delivered or measured, at what dose and duration? |
| Comparator | Was the comparison placebo, usual care, another exposure, baseline or absent? |
| Outcomes | Were they predefined, patient-important, surrogate, measured objectively or self-reported? |
| Results and uncertainty | What are the effect estimate, confidence interval, sample size and missing-data handling? |
| Limitations | What biases, confounders, imprecision or applicability limits did the authors report? |
Record the exact table, figure, page or section that supports the claim. If the AI says a study found an effect but the paper reports only an association, change the wording. If the source examined a laboratory marker rather than a clinical outcome, do not let a summary turn it into a health benefit.
Step 4: Test entailment and scope
Match the strength of the sentence to the evidence
Ask whether the cited passage supports the exact strength and direction of the statement. “Was associated with” is not interchangeable with “caused.” “In this sample” is not equivalent to “in adults generally.” A statistically significant estimate does not automatically establish practical importance, and a non-significant result does not prove that two interventions are equivalent.
Classify each claim as fully supported, supported only after qualification, unsupported, contradicted or out of context. A partially supported claim needs a narrower sentence and a new citation location, not a note that the reader must infer the caveat.
Use a second source for consequential claims
For claims that could change treatment, policy, funding or safety decisions, seek an independent primary source or authoritative synthesis where feasible. Agreement between two citations is not a substitute for reading them, but it can expose a single-study anomaly, an outdated result or a citation that was attached to the wrong conclusion.
Step 5: Verify numbers, quotations and tables
Numbers
Record each figure in a consistent form such as “20% — organisation, year,” then confirm it in the original publication. Check whether it is a relative or absolute effect, a percentage or percentage-point change, a mean or median, and whether the denominator, time frame and uncertainty interval were preserved. Recalculate simple transformations when practical, but retain the source’s definition and rounding.
If a reliable published figure cannot be found, say that no verified figure was located. Do not fill a gap with a plausible value or silently substitute a number from a different population.
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Quotations
Verify quotations word for word, including punctuation where it affects meaning. Confirm the speaker or author, role, date and surrounding context. A model may join fragments from different passages or invent a quotation that sounds characteristic. If exact wording cannot be confirmed, paraphrase the verified idea or remove the quotation.
Tables and derived calculations
Trace every cell to a source or calculation. Check units, denominators, duplicated participants and whether a model-derived value was presented as an observed result. Label estimates, assumptions and conversions so readers can distinguish them from reported findings.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchStep 6: Audit search completeness and reproducibility
Run the search yourself or have a second reviewer reproduce it. Save the database and web interfaces, search dates, complete queries, filters, language limits, date limits, deduplication rules and inclusion and exclusion criteria. Preserve the result counts at each stage and the reasons for exclusions.
- Write the question and eligibility criteria before screening the model’s suggestions.
- Run each named database search without letting the model silently alter the query.
- Export records in a stable format, remove duplicates with a documented rule and retain the pre-deduplication count.
- Screen titles and abstracts against piloted criteria, recording disagreements and resolutions.
- Retrieve full texts and record a specific exclusion reason for every excluded report.
- Search references backward and forward as a documented supplement. TARCiS provides 10 recommendations for naming and reporting these citation-searching methods.
- Store prompts, model and version, dates, inputs, outputs and any code or scripts used to rank, extract or classify records.
Reproducibility is about more than saving a prompt. Another reviewer should be able to determine which records the system saw, what instructions it received, what transformations occurred and where a human overruled it.
Step 7: Check the synthesis method
Use PRISMA 2020 to report why the review was undertaken, how studies were found and selected, what was excluded, and how results were synthesized. Include the flow of records and the characteristics of included studies. PRISMA structures transparent reporting; it does not certify that an AI-generated review is correct.
If AI suggested eligibility decisions, data extraction, risk-of-bias judgments, certainty ratings or statistical synthesis, identify the exact stage and keep the underlying outputs. Pilot the tool against records with known decisions, compare its errors with human decisions and explain how disagreements were resolved. A model must not be allowed to hide uncertainty by converting an ambiguous judgment into a definitive label.
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Step 8: Disclose AI use precisely
JAMA instructs authors to name the AI tool and version, manufacturer, dates of use, purpose and content affected, while making clear that authors reviewed and are accountable for the result. Its guidance also requires references to be checked for completeness, accuracy and integrity. ICMJE similarly states that humans remain responsible because AI output can be authoritative-sounding yet incorrect, incomplete or biased.
The Cochrane, Campbell, JBI and CEE position statement adds that evidence synthesists are responsible for the decision to use AI, how it is used, legal and ethical compliance, validation or piloting, prompts and outputs where practical, verification steps, limitations, conflicts and funding. Disclosure should be specific enough for a reader to understand whether AI helped search, screen, extract, analyse, write or edit.
We used {{tool_name}}, version {{version}}, from {{manufacturer}} between {{start_date}} and {{end_date}} to {{purpose}}. It affected {{stages_or_sections}}. We piloted the workflow on {{validation_set}}, independently verified references and claims against original sources, and recorded disagreements and corrections. The authors reviewed and take responsibility for all content. No confidential or identifiable material was entered without appropriate permission.Replace each field with factual information and remove the statement if it is not true. Do not claim that a tool was used only for spelling if it proposed eligibility decisions or substantive interpretations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 9: Protect confidentiality, privacy and rights
Do not paste privileged manuscripts, peer-review material, identifiable patient information or restricted text into a system unless you have permission, a suitable confidentiality agreement and an appropriate data-retention arrangement. ICMJE warns that uploading confidential manuscripts can breach editorial confidentiality. The same concern applies to unpublished trial data, proprietary datasets and text licensed for limited use.
- De-identify data only when doing so cannot undermine the analysis and the governing policy permits processing.
- Use the least sensitive input needed for the task; often a record identifier or structured field is sufficient.
- Check whether prompts and outputs may be retained or used for service improvement.
- Respect copyright and database licence terms when supplying full text to a model or redistributing its output.
- Keep a human-accessible copy of restricted material outside the model service.
Step 10: Obtain human sign-off
A qualified subject-matter expert should approve every high-stakes claim, the search and selection record, risk-of-bias judgments and the final reference list. The reviewer should have access to the original sources and the claim ledger, not just a polished manuscript.
- Confirm that every consequential claim has a verified, appropriate source.
- Check that the wording matches study design, population, outcome and uncertainty.
- Review retractions, corrections, conflicts of interest and funding disclosures.
- Inspect excluded studies and disagreements rather than accepting the model’s final labels.
- Approve the AI disclosure, privacy controls and permissions.
- Sign and date the final version, retaining the audit record and version history.
What performance numbers do—and do not—tell you
A 2025 review by Adel and Alani reported an average workload reduction of 60–65% across the studies it examined. In structured clinical screening tasks, title-and-abstract sensitivity ranged from 80.6% to 96.2%. In more interpretive tasks, precision was reported as low as 4.6%, and hallucination rates reached 91% in some literature-synthesis evaluations.
Those figures are not a universal accuracy score. Screening sensitivity, interpretive precision and hallucination rates measure different tasks under different conditions. A tool that reduces workload can still miss an eligible study, attach a citation incorrectly or introduce an unsupported conclusion. Set thresholds for the domain and review purpose, pilot them against a known record set and justify them in the methods. No single numerical threshold guarantees publication safety.
Choose a workflow that matches the stakes
| Workflow | Useful role for AI | Controls required | Appropriate use |
|---|---|---|---|
| Exploratory drafting | Suggest search terms, concepts and candidate papers. | All claims and references treated as unverified; no consequential decisions. | Early scoping and question refinement. |
| Controlled screening support | Prioritize or classify records after a human-defined protocol. | Piloted sensitivity, retained model outputs, duplicate or expert review of difficult records. | Large searches where missed studies can be detected and corrected. |
| Publication-grade synthesis | Assist with extraction or wording inside a documented protocol. | Source-level verification, reproducible searches, risk-of-bias review, disclosure, privacy controls and expert sign-off. | Systematic reviews, clinical guidance and policy evidence. |
Compare workflows on source coverage, metadata accuracy, claim-to-source entailment, retraction and limitation handling, risk-of-bias and certainty judgments, reproducibility, disclosure, confidentiality, reviewer expertise, time saved and errors introduced. The strongest workflow leaves an inspectable record for every decision rather than merely producing a convincing narrative.
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Common failure modes and repairs
“The DOI resolves, so the citation is valid.”
A resolving DOI proves that a record exists, not that its authors, year or findings were represented correctly. Open the record and read the relevant passage.
“The abstract says the same thing.”
Abstracts may omit exclusions, subgroup definitions, missing data and limitations. Retrieve the full text for claims that drive the conclusion.
“The model cited many papers, so the review is comprehensive.”
Volume is not coverage. Reproduce the database searches, citation chasing and eligibility decisions, then report what was missed or excluded.
“A fluent causal sentence is a harmless simplification.”
Replace causal language with the design-appropriate association or estimate, and retain uncertainty and applicability limits.
“PRISMA makes the review trustworthy.”
PRISMA makes reporting more transparent; it cannot validate fabricated sources, incorrect extraction or biased judgments. Complete the source and claim checks separately.
“Disclosure can wait until the end.”
Record tool versions, dates, prompts, outputs and human interventions during the work. Reconstructing them after publication is unreliable and may omit important controls.
A final release checklist
- Every sentence has been reduced to checkable claims.
- Every citation exists, resolves and matches its metadata.
- Every important claim has been compared with the original evidence.
- Association, causation, population, outcome and uncertainty are stated accurately.
- Numbers, quotations, tables and derived calculations have been independently verified.
- Searches, filters, screening decisions and citation-chasing methods are reproducible.
- AI-assisted eligibility, extraction, risk-of-bias or synthesis judgments are disclosed and validated.
- Confidential, identifiable and restricted material was protected.
- A qualified human approved the claims, methods, references and disclosure.
The defensible standard
An AI-assisted literature review is ready only when a reader can follow each material claim to a real source, inspect why that source supports the wording, reproduce how the evidence was found and understand where AI influenced the work. If any link is missing, narrow the claim, locate better evidence or remove it. Human authors—not the model—remain accountable for accuracy, attribution, permissions, confidentiality, plagiarism prevention and the integrity of the final synthesis.
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