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AI is not a universal substitute for human peer review. It can accelerate bounded parts of a literature review—especially scanning, summarising and first-pass checks—and can produce repeatable screening signals. Humans remain essential for deciding whether evidence is eligible, judging methods and context, integrating findings across studies, protecting confidential manuscripts and accepting editorial responsibility. The safest model is supervised use: let AI assist with auditable routine work, verify every consequential claim against primary papers, disclose judgment-shaping automation and keep final decisions with qualified people.
These are two different activities
The phrase “AI research review” can describe AI-assisted evidence synthesis, while “AI peer review” usually means using a model to evaluate a manuscript submitted to a journal. They have different inputs, goals, error patterns and rules. A literature reviewer starts with a defined question and a body of published or otherwise discoverable studies. A peer reviewer receives a confidential manuscript and advises an editor about its validity, originality, clarity and suitability for a venue.
AI-assisted evidence synthesis
In this workflow, a researcher may use software or language models to find candidate papers, extract information, translate terminology, summarise passages, organise notes or suggest themes. The researcher still has to set the protocol, confirm that a paper meets the eligibility criteria, inspect the original methods and results, reconcile conflicting findings and write a reproducible synthesis. The four major evidence-synthesis organisations—Cochrane, the Campbell Collaboration, the Joanna Briggs Institute and the Collaboration for Environmental Evidence—state that automation is acceptable only when methodological rigour and integrity are maintained, with human oversight and transparent reporting whenever AI makes or suggests judgements. Their joint position is available at https://pmc.ncbi.nlm.nih.gov/articles/PMC12577299/.
AI as a manuscript peer reviewer
Here, a model is asked to critique a paper, recommend acceptance or rejection, identify methodological concerns or draft comments. The material may be unpublished and covered by a journal’s confidentiality rules. A model can flag a possible issue, but it cannot assume the reviewer’s duty of care, understand every disciplinary convention or take responsibility for an editorial recommendation. Current Springer Nature guidance, accessed 13 October 2026, says scholarly judgement, accountability and responsibility remain human and lists delegating peer review to a large language model among disallowed uses under its framework. A journal’s own live policy controls, so check it before uploading any manuscript.
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What the available comparisons actually found
A UK evidence-review case study
A Behavioural Insights Team exercise commissioned by the UK Department for Science, Innovation and Technology compared one AI-assisted rapid review with one human-only rapid review on technology diffusion and UK growth and productivity. The underlying reviews were conducted in 2024 and the results were published on 23 April 2025. The AI-assisted workflow used several tools, not one named model, and included manual checking. It took 90.5 hours compared with 117.75 hours for the human-only workflow—about 23% less total time in this particular pair of reviews. Because there was one review per approach, the result is a case study rather than a typical or guaranteed saving. See the full UK comparison.
| Review phase | AI-assisted workflow | Human-only workflow | What the comparison suggests |
|---|---|---|---|
| Total | 90.5 hours | 117.75 hours | 23% less time for the AI-assisted case |
| Analysis | 15 hours | 34 hours | 56% less analysis time in this case |
| Selection | 14 hours | 10 hours | AI assistance did not make eligibility decisions faster |
| Revisions | 27 hours | 18.25 hours | More revision time was required after the AI-assisted workflow |
The pattern matters more than the headline percentage. AI-supported scanning and analysis were faster, but selection took four additional hours and revisions took 8.75 additional hours. Reviewers also reported that the human-only version produced a more integrated narrative. AI summaries were often presented study by study, so the team had to do more work to connect evidence, qualify differences and create a coherent argument.
A 22-manuscript peer-review comparison
A 2025 cross-sectional study examined 22 submissions to the Journal of Oral and Maxillofacial Surgery received from January through December 2023 and compared human decisions with four AI models. Human reviewers rejected 15 of 22 manuscripts (68.2%). The models’ rejection rates ranged from 0% to 9.1%. Agreement between AI recommendations and human recommendations was moderate, with weighted kappa values from 0.38 to 0.46. The study’s PubMed record is at https://pubmed.ncbi.nlm.nih.gov/40252692/.
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The models were highly consistent when assessed again 24 hours later (intraclass correlation coefficient 0.88). That is evidence of repeatability, not proof of correctness. A system can produce the same mistaken or shallow assessment every time. The sample was small, confined to one specialty and based on four model versions, so it cannot establish how AI performs in other fields, journals or languages. The authors proposed AI as a screening complement to expert review, not a replacement.
AI and humans compared by the decisions that matter
| Criterion | AI-assisted work | Human-led work | Practical implication |
|---|---|---|---|
| Speed | Can accelerate scanning, extraction and some analysis; the UK case was faster overall. | Usually slower for repetitive processing. | Use automation for bounded throughput gains, then budget time for checking and revision. |
| Eligibility and selection | May apply a rule consistently but can misread nuanced inclusion criteria; the UK AI workflow took longer here. | Can interpret protocol exceptions and ambiguous designs. | Keep final inclusion and exclusion decisions with trained reviewers and record reasons. |
| Accuracy and error detection | May miss fabricated references, altered numbers, retracted work or context; a confident answer is not evidence. | Can inspect the actual paper, supplementary material, data and discipline-specific conventions. | Trace every important statement to the primary source. |
| Cross-study synthesis | Good at producing separate summaries; may leave contradictions and causal differences unintegrated. | Better positioned to explain heterogeneity, study quality and limits of inference. | Use model output as notes, never as the finished synthesis. |
| Repeatability | The manuscript study found 24-hour temporal consistency of ICC 0.88. | People can disagree, but discussion can expose assumptions and correct errors. | Use repeated AI passes as a flagging mechanism, not as a validity test. |
| Verification burden | Can reduce reading of routine passages while adding fact-checking and revision work. | Requires more first-pass reading but may reduce unsupported synthesis. | Measure total verified time, not model output time alone. |
| Confidentiality | Uploading an unpublished manuscript may breach a journal’s rules or expose sensitive content, depending on the service and settings. | Human reviewers are bound by the venue’s confidentiality terms. | Obtain permission and follow the journal’s current policy before submitting any manuscript text to a tool. |
| Accountability | Cannot be the author, reviewer of record or responsible decision-maker. | People own the protocol, recommendation, disclosure and correction process. | Assign a named human who can explain and defend every consequential judgement. |
There is no single head-to-head benchmark covering all disciplines and all of these criteria. Performance depends on the task, field, prompt, model version, source access, language and verification procedure. The available studies support a cautious division of labour, not a universal ranking.
Where AI assistance is genuinely useful
Scanning a defined body of literature
Once a search strategy and inclusion rules exist, an AI system can help group records, identify likely duplicates, produce short candidate summaries and surface terms that a human may have overlooked. The UK comparison found particular time savings in analysis after researchers checked and probed the generated summaries. Keep the original search results and do not treat a generated abstract as a substitute for reading the paper.
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Routine language and accessibility work
American Psychological Association guidance discusses grammar cleanup, syntax and readability support, translation of academic terminology and assistance with finalisation tasks. These operations can improve communication without transferring responsibility for the underlying claims. APA also warns that researchers must vet output, retain control, cite AI use where appropriate and remain alert to fabricated studies. Its guidance is at https://www.apa.org/topics/artificial-intelligence-machine-learning/ai-research-writing.html.
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Structured first-pass checks
A model can be prompted to look for missing outcome definitions, inconsistent sample counts, absent control groups, unexplained statistical terms or a mismatch between an abstract and a conclusion. Such a checklist is useful when it prompts a person to inspect the relevant page, table or appendix. It is not an audit certificate.
Question formulation and early exploration
AI can suggest alternative search terms, possible mechanisms or gaps worth investigating. Every suggestion must be validated against real papers because a plausible-sounding title, citation or summary may be fabricated or may conflate separate studies.
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Where human judgement cannot be delegated
Protocol-sensitive eligibility decisions
Inclusion criteria often contain exceptions: a study may use the right population but the wrong outcome window, or contain usable subgroup data only in a supplement. A reviewer must interpret the protocol, document the reason for a decision and resolve disagreements. The UK case’s longer AI-assisted selection phase is a concrete warning against assuming that automation improves every stage.
Methodological appraisal
Determining whether a result is credible requires examining design, recruitment, missing data, measurement validity, statistical assumptions, selective reporting and conflicts of interest. A model can describe these features, but it cannot replace a domain expert who decides whether they undermine the inference.
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Integrating conflicting evidence
A useful synthesis explains why studies disagree: different populations, exposures, comparators, follow-up periods, definitions or risk of bias can produce apparently inconsistent results. The UK reviewers found that isolated AI summaries required more revision to become an integrated narrative. A human author must make the comparison explicit and qualify the conclusion.
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Editorial recommendations and responsibility
Accept, revise or reject decisions affect authors’ careers and the scholarly record. A reviewer must judge importance, ethical compliance, reporting quality and fit with the journal, then provide fair, specific comments. No model can accept legal, ethical or professional responsibility for that recommendation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A supervised workflow for an AI-assisted evidence review
- Write the protocol first. Define the question, databases or sources, dates, population, intervention or exposure, comparator, outcomes, study designs, exclusion rules and planned synthesis. Do not let a model silently rewrite these criteria.
- Run and preserve the search. Save exact queries, result exports, deduplication rules and the date of each search. AI-generated search terms can supplement, but not replace, a documented strategy.
- Use AI only for a named task. Specify whether it is classifying titles, extracting a field, translating terminology or drafting a summary. A narrow task is easier to validate than an open-ended request to “review the literature.”
- Keep source-linked records. Store the paper identifier, page or table location, the model output and the reviewer’s verification status. Never retain a claim without a path back to the original text or data.
- Verify eligibility manually. Read the relevant methods and results for every included study and for borderline exclusions. Record the protocol rule that determined each decision.
- Check extracted numbers twice. Compare sample sizes, effect estimates, confidence intervals, units, dates and subgroup labels with the source. Investigate discrepancies rather than asking the model to choose.
- Appraise methods and context. A qualified reviewer evaluates bias, applicability, heterogeneity and the limits of causal or predictive claims. Use AI to present a checklist, not to assign an unexamined quality score.
- Draft the synthesis from verified notes. Organise findings by question, comparison or mechanism. Explain disagreements and uncertainty; do not paste isolated model summaries into a conclusion.
- Audit the final manuscript. Check every citation, quotation, number and interpretation against the source papers. Search specifically for invented references and statements that exceed the evidence.
- Report AI use transparently. Name the tool or service, version or access date when relevant, task, prompts or procedure where needed for reproducibility, human checks and any material limitations. The evidence-synthesis position statement requires full, transparent reporting when AI makes or suggests judgements.
A safe workflow for journal peer reviewers
- Read the journal’s current policy and confidentiality agreement. Policies differ and can change. Springer Nature’s framework, for example, retains scholarly judgement and accountability with people and disallows delegating peer review to a large language model; another venue may set different boundaries.
- Ask the editor before using any tool. Do not assume that anonymising a manuscript makes uploading it permissible. Figures, rare cases, datasets and supplementary files can identify authors or patients.
- Separate private notes from submitted comments. If an approved tool is used for language or formatting help, inspect the output line by line. Do not allow it to invent criticisms, citations or claims about data you have not examined.
- Evaluate the manuscript yourself. Check the research question, design, analysis, reporting, ethics, data availability, figures and conclusions. Use an AI-generated checklist only as a prompt for inspection.
- Write the recommendation in your own judgement. Explain major and minor issues with page, table or figure references. The editor should be able to see why the recommendation follows from the manuscript, not from a model score.
- Disclose permitted use. Follow the journal’s required statement, including what the tool did and how you checked it. If the policy prohibits AI assistance, do not use it on the manuscript.
Common failure modes and practical fixes
| Failure mode | Why it happens | Fix |
|---|---|---|
| Fabricated or mismatched citations | The model predicts a plausible bibliographic sequence rather than retrieving a verified record. | Open every cited paper, confirm title, authors, publication details and the exact supporting passage; remove anything that cannot be verified. |
| Correct number, wrong meaning | A result is detached from its population, denominator, unit, time point or adjustment. | Copy the surrounding methods and table labels into your verification notes and preserve all qualifiers. |
| Confident eligibility errors | Natural-language criteria contain exceptions or depend on details absent from the abstract. | Require a human decision and a recorded protocol reason for each borderline record. |
| Summary without synthesis | The model treats each paper as an isolated item and misses heterogeneity. | Build a comparison matrix, then have a subject expert explain differences in design, population and risk of bias. |
| Stable but systematically wrong recommendations | Repeated prompts reproduce the same hidden bias or omission. | Use independent human checks and, where appropriate, compare with a second reviewer; never interpret consistency as accuracy. |
| Confidentiality breach | Unpublished manuscript text or sensitive data is sent to an unauthorised service. | Follow the venue’s policy, obtain explicit permission and use only approved settings or tools; otherwise do not upload the material. |
| Undisclosed judgment-shaping automation | AI influences screening, appraisal or conclusions but is described only as “editing.” | Disclose every role that could affect a decision and identify the human responsible for checking it. |
How to choose the right level of automation
| Task | Appropriate default | Minimum human control |
|---|---|---|
| Grammar, readability or terminology translation of your own text | AI assistance can be reasonable. | Author verifies meaning, citations, confidentiality and required disclosure. |
| Deduplication, tagging and candidate sorting in a documented review | AI or automation can provide a first pass. | Validate against the protocol; manually review uncertain and excluded records. |
| Data extraction from included papers | Use only with source-linked fields and an audit trail. | Check every consequential value against the paper and resolve discrepancies. |
| Risk-of-bias or methodological appraisal | Human-led, with AI as a checklist or question generator. | Qualified reviewers make and justify the judgement. |
| Cross-study interpretation and conclusions | Human-led synthesis. | Explain heterogeneity, uncertainty and applicability from verified evidence. |
| Confidential journal manuscript review | Follow the journal’s policy; do not assume AI is allowed. | Human reviewer owns the recommendation and confidentiality obligations. |
How to interpret the headline numbers without overclaiming
The 23% overall time difference and 56% analysis difference from the UK exercise are observed differences between two reviews, not a productivity guarantee. The extra selection and revision time demonstrates why “hours saved by the model” is not the same as “hours saved by the project.” A study with harder eligibility rules, poorer source access or more contradictory findings could have a different balance.
The peer-review figures are similarly bounded. In 22 oral and maxillofacial surgery submissions, humans rejected 68.2% while the four models rejected 0–9.1%, and weighted agreement was only moderate (κ 0.38–0.46). Those numbers do not prove that humans are always right or that models are always lenient; they show that the tested recommendations diverged substantially in that sample. The ICC of 0.88 says the model outputs were temporally consistent, not that they matched an external gold standard.
What responsible disclosure looks like
Disclose AI use whenever it materially shapes a search, screening decision, extraction, appraisal, synthesis or manuscript text, following the relevant journal, funder and institutional rules. A useful record states the service and version or access date, the task it performed, whether it generated or suggested judgements, the human verification procedure and any known limitations. Keep prompts, exports and decision logs when they are needed to reproduce the work, while removing confidential or personal data that you are not authorised to store.
APA’s guidance includes the principle that authors must remain in control of their work; its publisher Rose Sokol summarises the boundary as, “To be an author you must be a human.” The evidence-synthesis organisations similarly state that “AI and automation in evidence synthesis should be used with human oversight” and that any use making or suggesting judgements should be fully and transparently reported. These are governance requirements, not performance claims.
Bottom line for researchers, editors and reviewers
Use AI where the task is bounded, repeatable and easy to verify: organising records, suggesting search terms, producing provisional summaries, checking language and flagging possible inconsistencies. Do not hand it the parts that determine what counts as evidence, whether a method supports an inference, how conflicting studies should be integrated or whether a confidential manuscript deserves publication. A supervised workflow can deliver real efficiency, but the person who defines the protocol, checks the sources, explains the reasoning and signs the decision remains accountable for the scholarly record.
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