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AI is useful as a supervised assistant for bounded review tasks, but current evidence does not support treating it as a universal replacement for human peer review. It can accelerate literature scanning, summarisation, language cleanup and repeatable first-pass checks. People must still verify claims against the original papers, judge methods and context, integrate findings across studies, protect confidential material and accept responsibility for the final decision.
“AI research review” and “AI as a manuscript peer reviewer” are different activities. The first uses automation inside an evidence-synthesis workflow; the second asks a system to assess a submission for a journal. Their evidence, risks and permissible uses differ, so they should not be evaluated as one question.
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What is being compared?
AI-assisted evidence review
An evidence review asks a bounded question, finds relevant studies, screens them, extracts information, appraises methods and produces a synthesis. An AI-assisted workflow may use several tools to search or organise records, summarise selected papers, translate terminology, suggest coding or help draft prose. The human team remains responsible for the protocol, eligibility decisions, interpretation and citations.
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Peer review evaluates a specific manuscript for a journal. A reviewer may be asked to identify methodological flaws, unsupported conclusions, missing literature, ethical concerns or presentation problems, then recommend a decision. That work involves confidential material and a formal editorial relationship. A model that produces a plausible critique is not thereby qualified to make the editorial judgment, and a reviewer may be barred from uploading the manuscript to an external service.
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Bottom line by decision
| Question | Best-supported answer |
|---|---|
| Can AI reduce time in parts of an evidence review? | Yes, in bounded workflows. A 2024 UK case study found lower total time, but it covered one topic and one review by each approach. |
| Does a stable AI score mean a correct score? | No. Stability measures repeatability, not validity or sound editorial judgment. |
| Should AI make final eligibility, appraisal or publication decisions? | No. Those decisions require accountable human judgment and checking against source material. |
| Can a peer reviewer paste a manuscript into any AI tool? | Not safely. Check the journal’s current confidentiality and AI policy first; some policies prohibit delegating review to a language model. |
| Is there a universal accuracy or success rate for AI versus people? | No. Performance varies with task, field, model, prompts, source access and verification. |
What the time evidence actually shows
The clearest workflow comparison comes from a Behavioural Insights Team exercise commissioned by the UK Department for Science, Innovation and Technology and the Department for Digital, Culture, Media and Sport. Published on 23 April 2025, it compared two rapid reviews on technology diffusion and UK growth/productivity. The underlying reviews were conducted in 2024: one used AI assistance and one used a human-only workflow.
The AI-assisted review took 90.5 hours, compared with 117.75 hours for the human-only review—about 23% less overall. That is a case-study result, not a forecast of typical savings across subjects or teams. The workflow used multiple tools, so the difference cannot be attributed to one named model.
| Review phase | AI-assisted | Human-only | What the comparison implies |
|---|---|---|---|
| Analysis | 15 hours | 34 hours | AI-assisted analysis was 56% lower in this exercise, after researchers checked and probed the summaries. |
| Selection | 14 hours | 10 hours | Screening took longer with AI assistance because manual checking remained part of the method. |
| Revisions | 27 hours | 18.25 hours | The AI-assisted draft needed more revision to integrate material and correct or refine output. |
| Total | 90.5 hours | 117.75 hours | Approximately 23% less time for the AI-assisted review in this two-review comparison. |
The pattern matters more than the headline percentage. AI helped most during analysis, while selection and revision became more demanding. Faster summaries do not remove the work of deciding whether a paper meets the protocol or whether separate findings support a coherent conclusion.
Where AI can help in an evidence review
Scanning and summarising a defined set of papers
Once a team has a reliable set of records, an AI system can produce structured summaries, extract recurring concepts or flag passages for inspection. In the UK exercise, researchers reported faster scanning and especially faster analysis when they interrogated AI-generated summaries and checked them against the papers. The useful unit is a verified summary, not an unchecked model answer.
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- Give the system a fixed, identifiable document set rather than asking it to invent a bibliography.
- Require page, section or table locations for every extracted claim.
- Open the original paper before accepting a number, quotation, definition or causal statement.
- Keep a record of prompts, model version, date and corrections so another reviewer can reproduce the step.
Routine language and workflow assistance
Guidance from the American Psychological Association describes potential uses such as grammar and syntax cleanup, readability edits, translation of academic terminology and help with finalisation tasks. These operations can reduce friction without transferring responsibility for the scholarly claims. A polished paragraph can still misstate a result or imply a causal relationship that the study did not test.
Repeatable first-pass checks
Repeated prompts can make a model’s first-pass output stable enough to act as a screening aid. That may help a human reviewer notice a possible inconsistency, missing control or unclear definition. Stability should be treated as a reason to inspect an issue, not as proof that the issue exists or that the model’s rating is right.
Question formation and early exploration
AI can suggest search terms, possible mechanisms or gaps when a project is being scoped. APA guidance cautions that literature suggestions and summaries must be validated against the actual papers, including checking that a cited study exists and says what the output claims. Use exploration to generate hypotheses and search strategies, not to establish the evidence base.
Where human judgment remains essential in evidence synthesis
Eligibility and protocol-sensitive selection
Selection is not merely a text-matching exercise. Inclusion may depend on population, intervention, comparator, outcome, design, date, language or a protocol amendment. The UK comparison took 14 AI-assisted hours versus 10 human-only hours in selection, partly because people had to check the automated suggestions. A system can miss an eligible paper, include a near match or apply an ambiguous criterion inconsistently.
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Methodological appraisal
A summary cannot establish whether a study’s sampling, measurement, statistical model or risk-of-bias controls are adequate. Reviewers must examine the methods and determine whether the inference follows from the design. The joint position of Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence says AI and automation are acceptable only when methodological rigour and integrity are maintained, with human oversight: Position statement on artificial intelligence (AI) use in evidence synthesis.
Cross-study synthesis and context
Evidence synthesis requires comparing unlike populations, definitions, measures and settings, then explaining why results agree or conflict. In the UK exercise, the human-only version was described as having a more integrated narrative; AI summaries were often presented in isolation and required additional revision. A collection of accurate summaries is not automatically an explanation of the evidence.
Detecting fabricated or distorted support
Models can produce a convincing citation that does not exist, attach the wrong result to a real paper or omit a qualification such as “exploratory” or “not statistically significant.” APA’s guidance explicitly warns about fabricated studies. Verify every reference, quotation, number and interpretation in the source material, and retain the source location in your notes.
What the manuscript peer-review evidence shows
A 2025 cross-sectional study examined 22 submissions to the Journal of Oral and Maxillofacial Surgery submitted from January through December 2023. The authors compared the journal’s human decisions with the recommendations of four AI models. The PubMed record for “How Artificial Intelligence Differs From Humans in Peer Review” reports a sharp difference in rejection rates:
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| Reviewer type | Rejections | Rejection rate |
|---|---|---|
| Human reviewers | 15 of 22 manuscripts | 68.2% |
| Four AI models | Varied by model | 0–9.1% |
Agreement between AI recommendations and human recommendations was only moderate, with weighted kappa values of 0.38–0.46. The models’ ratings were highly consistent when repeated after 24 hours (intraclass correlation coefficient 0.88). That temporal consistency means the same prompt tended to produce a similar rating; it does not show that the rating was correct, that important flaws were detected or that the model understood the journal’s standards.
The sample was small, limited to one specialty and four model versions described in that study. It cannot establish how models would perform in another field, with different instructions or against a different editorial standard. The authors’ practical implication was screening that complements expert review, not replacement of expert review.
Eight comparison axes that matter
| Axis | AI-assisted review | Human-led review | Practical question |
|---|---|---|---|
| Time by phase | Can be faster for scanning and analysis; may add checking time in selection and revision. | Usually slower for repetitive extraction but may spend less time correcting integration problems. | Which phase is the bottleneck in your protocol? |
| Accuracy and error detection | Can miss context, invent support or reproduce a prompt-induced error. | Can inspect methods and sources directly, but people also miss errors and disagree. | What independent verification catches a wrong answer? |
| Coverage and selection | May process many records quickly but can apply ambiguous criteria poorly. | Can interpret edge cases and document reasons, with limited throughput. | Who resolves borderline eligibility decisions? |
| Cross-study synthesis | Good at candidate summaries and comparisons; integration may be fragmented. | Better positioned to connect design, context and competing explanations. | Who writes and defends the overall inference? |
| Repeatability | Can produce stable first-pass outputs; stability is not validity. | May vary between reviewers, making calibration and adjudication necessary. | Do repeated checks improve reliability without hiding systematic error? |
| Verification burden | Every substantive output needs source checking and correction. | Requires training, double-coding or adjudication but can explain judgments. | Is the saved time greater than the checking time? |
| Confidentiality and transparency | Data handling, retention and disclosure depend on the service and policy. | Human reviewers still need confidentiality controls and conflict declarations. | Can you lawfully and ethically place the material in the chosen system? |
| Accountability | Cannot be the responsible author, reviewer or editor. | People remain answerable for methods, recommendations and decisions. | Who signs off and can explain the decision? |
No source cited here provides a head-to-head benchmark spanning all fields and all of these tasks. The right comparison is therefore task-specific rather than a universal ranking.
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A safe, AI-assisted evidence-review workflow
- Write the protocol first. Define the question, databases or sources, eligibility criteria, outcomes, extraction fields, risk-of-bias method and adjudication process before asking a model to assist.
- Collect and freeze the candidate set. Store stable identifiers and full texts where permitted. Do not let an AI-generated list silently become the search universe.
- Use AI for a bounded operation. Examples include classifying abstracts against explicit criteria, proposing extraction fields or drafting a plain-language summary of a supplied paper. State what the system may and may not decide.
- Demand traceability. Ask for the exact page, section, table or figure supporting each extracted item. Mark anything for which the system cannot provide a location as unverified.
- Have people verify eligibility and data. Inspect the original record, resolve borderline cases and record exclusions and reasons. For high-stakes reviews, use independent reviewers and an adjudicator.
- Appraise methods independently. Read the methods and results, apply the planned risk-of-bias tool and document disagreements. Never infer methodological quality from a summary score alone.
- Synthesise with context. Compare populations, interventions, measures, follow-up periods and limitations. Treat model-generated themes as prompts for analysis, not as findings.
- Audit the draft. Check every citation, quotation, number, table cell and claim of causality against the source paper. Remove unsupported confidence and disclose corrections.
- Document the AI role. Record the tool or service, version if available, dates, prompts or prompt categories, input limits, outputs used, human checks and any material changes. The Cochrane, Campbell, JBI and CEE position says any AI use that makes or suggests judgments should be fully and transparently reported.
- Keep a human sign-off. A named researcher accepts responsibility for the protocol, data, synthesis and final wording.
How to handle AI when you are a journal peer reviewer
- Read the journal’s live policy before opening the manuscript in a tool. Policies differ and can change. Check rules on confidentiality, external processing, disclosure, permitted language editing and whether AI may assist with a review.
- Separate private notes from manuscript content. If a policy permits limited assistance, minimise pasted material, remove unnecessary identifying information and use only an approved environment. Do not assume that a “free” interface keeps submissions confidential.
- Use the model, if allowed, as a checklist. Ask it to suggest questions about study design, reporting completeness or unclear passages. Do not delegate the recommendation or paste its text into the review without checking it.
- Verify every suggested criticism. Read the relevant method, result or cited paper yourself. A false positive can unfairly damage a submission; a false negative can let a serious flaw pass.
- Write the review in your own accountable voice. Explain the evidence for each major concern, distinguish fatal from fixable problems and disclose permitted AI assistance as the journal requires.
- Do not upload confidential manuscripts where the venue forbids it. Current Springer Nature guidance states that scholarly judgment, accountability and responsibility remain human and lists delegating peer review to a large language model among disallowed uses under its framework. That is one publisher’s policy framework, so consult the specific journal rather than assuming it applies everywhere.
Choosing the right level of automation
| Task | Reasonable AI role | Human control required |
|---|---|---|
| Grammar, syntax and readability | Suggest edits or alternative wording. | Ensure meaning, technical terms, authorship and claims are unchanged. |
| Search-term brainstorming | Generate synonyms, related concepts and possible gaps. | Build and document the actual search strategy; test recall and precision. |
| Abstract triage | Prioritise records or flag likely exclusions. | Apply criteria, inspect borderline records and retain exclusion reasons. |
| Data extraction | Populate a draft form with source locations. | Verify values, units, denominators, missing data and transcription. |
| Risk-of-bias appraisal | Suggest questions to ask. | Read the methods and make the rating under the prespecified tool. |
| Cross-study synthesis | Cluster concepts or propose comparisons. | Interpret heterogeneity, limitations and strength of evidence. |
| Journal recommendation | At most, provide a permitted checklist. | Human reviewer and editor make and defend the decision. |
Verification checklist for every AI-generated claim
- Can you open the cited paper or record, and does it actually exist?
- Does the quoted wording appear in the source, with the same qualifiers?
- Do the number, unit, denominator, date and population match the source table or text?
- Has a correlation been turned into a causal claim?
- Has a null, uncertain or exploratory result been presented as definitive?
- Were contradictory studies or relevant limitations omitted?
- Would another reviewer following the documented protocol reach the same inclusion, extraction or appraisal decision?
- Is the use permitted by the funder, institution, journal and data-protection rules?
Costs, requirements and practical constraints
The evidence above does not establish a universal price, hardware requirement or model specification. What matters operationally is access to the source documents, a reproducible record of tool use, a reviewer capable of checking the output and enough time for verification. A tool that saves analysis time but creates extensive correction work may not reduce the total project burden. Likewise, a high-consistency model can still encode a systematic blind spot.
For confidential manuscripts or unpublished participant data, the governing constraint is usually policy and data handling rather than model capability. Obtain explicit permission where required, minimise data exposure and preserve a human audit trail.
What would stronger evidence look like?
The two-review UK exercise demonstrates a plausible workflow advantage, not a typical savings rate. The manuscript study demonstrates differences in recommendation behaviour and repeatability in 22 oral and maxillofacial surgery submissions, not general peer-review accuracy. Policy statements from professional organisations describe acceptable governance, not comparative performance tests.
Useful future evaluations would preregister protocols, compare the same documents and prompts, include multiple fields and model versions, measure missed errors as well as time, report verification effort, and assess whether final decisions improve. Until such studies exist, the defensible position is supervised assistance: automate routine, bounded work; verify against primary papers; disclose judgment-shaping use; and keep methodological and editorial accountability with people.
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Frequently Asked Questions
Is AI peer review as good as human peer review?
There is no general answer or universal success-rate statistic. In one 22-manuscript oral and maxillofacial surgery study, humans rejected 68.2% of submissions while four AI models rejected 0–9.1%, with only moderate agreement (weighted κ 0.38–0.46). The sample and specialty are too narrow to rank either approach everywhere.
What does an intraclass correlation of 0.88 mean in the manuscript study?
The AI ratings were highly consistent when repeated 24 hours later. It measures repeatability, not correctness, sensitivity to important flaws or quality of editorial judgment.
How should authors disclose AI use in an evidence synthesis?
Describe the tool or service, its version when available, dates and the task it performed; explain whether it made or suggested judgments; state how people verified and corrected the output; and retain enough documentation for the workflow to be understood and reproduced. Follow the reporting rules of the target journal or organisation.
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