AI-Assisted Peer Review: Uses, Risks & Ethical Guidelines

What Is AI-Assisted Peer Review? Uses, Benefits, Risks, and Ethical Guidelines

Artificial intelligence is changing how researchers search the scientific literature, organize information, and prepare academic manuscripts. It is also creating new possibilities and challenges for the peer-review process.

Reviewers may be interested in using AI tools to improve the clarity of their feedback, prepare general methodological checklists, or explore questions related to research design.

However, peer review involves access to confidential, unpublished research. Uploading a manuscript into an unauthorized AI system may expose sensitive information, compromise intellectual property, or violate a journal's editorial policies.

AI-assisted peer review refers to using artificial intelligence technologies to support selected activities in manuscript evaluation while preserving independent human judgment and accountability.

Appropriate AI assistance may involve organizing a reviewer's own notes, improving language, or consulting general background information. It should not replace expert evaluation of the study's methods, findings, and scientific contribution.

Major publishers, including Springer Nature, Elsevier, and Wiley, have established policies addressing how AI may be used during peer review.

These policies vary, but they share an important principle: reviewers remain personally responsible for the accuracy, integrity, and scientific reasoning of their reports.

This guide explains how AI-assisted peer review works, its potential benefits, major risks, publisher policies, practical examples, and responsible-use guidelines for academic reviewers.

What Is AI-Assisted Peer Review?

AI-assisted peer review is the use of artificial intelligence to support defined tasks within the academic manuscript-review process.

These tasks may include improving written communication, organizing reviewer-generated observations, or helping researchers locate general methodological guidance.

Some journals and publishers also use specialized editorial technologies to support submission screening, reviewer identification, and other administrative activities.

However, these editorial systems are different from a reviewer independently uploading a confidential manuscript into a public AI chatbot.

The distinction is important because peer reviewers receive unpublished research under confidentiality obligations.

An AI tool may produce fluent explanations or identify general scientific concepts, but this does not establish that it can reliably evaluate the validity of a particular study.

For example, a researcher reviewing an electrical engineering manuscript might use an authorized AI tool to help improve the organization of comments they have independently written.

That differs fundamentally from asking a chatbot to read the submitted manuscript and recommend acceptance or rejection.

AI can support the review process, but responsibility for the review must remain with the human expert.

How Is AI Used in Academic Peer Review?

Artificial intelligence can support several activities connected with manuscript reviewing.

The acceptability of each use depends on the journal's policies, the tool's data protections, and whether confidential information is involved.

1. Improving the Language of Reviewer Reports

Reviewers sometimes identify valid scientific concerns but struggle to express them clearly.

AI-based language assistance may help improve grammar, sentence structure, and professional tone.

For example, a reviewer might write:

"The statistics are bad and the conclusions don't make sense."

A more constructive version would be:

"The manuscript should clarify the statistical assumptions and explain how the reported estimates support the conclusions."

An AI tool may help improve this wording, but the scientific concern must originate from the reviewer.

Even language editing requires care. A reviewer's draft may reveal confidential findings or details about the submitted manuscript.

Use only a workflow permitted by the journal, and do not share confidential text with an unauthorized tool.

2. Organizing Review Comments

A reviewer may prepare extensive notes about the introduction, methodology, results, and discussion.

AI-assisted organization can help transform suitable, nonconfidential notes into a clearer structure.

For example, comments may be grouped into major methodological concerns and minor presentation issues.

This can make the final report easier for editors and authors to understand.

However, some publishing policies restrict using AI with any review material containing information about the submitted manuscript.

The reviewer must follow the actual journal policy before using this approach.

3. Generating General Evaluation Checklists

AI can help prepare a general checklist based on public research-reporting standards.

For example, a reviewer might consult guidance on randomized clinical trials, observational studies, systematic reviews, or qualitative research.

Common reporting standards include CONSORT, STROBE, PRISMA, and COREQ.

AI can help organize publicly available checklist criteria, but the reviewer should verify each requirement against the official standard.

The tool should not independently decide whether a confidential manuscript complies with those standards.

4. Supporting Background Literature Searches

Some AI-powered search tools can help researchers discover relevant published literature.

A reviewer may use such tools to locate publicly available studies or methodological references.

However, AI-generated citations can be inaccurate, incomplete, or fabricated.

Every source should be checked using the original publication or a reliable scholarly database.

A reviewer should not include a reference simply because an AI system suggested it.

5. Exploring General Methodological Questions

AI may help reviewers reflect on general research methods.

For example, a reviewer can ask for an explanation of when a mixed-effects model is generally appropriate or what assumptions apply to a particular statistical test.

The question must not reveal confidential manuscript details.

The reviewer then uses independent expertise to determine whether the information is relevant.

This is different from asking AI to evaluate the methods of an unpublished manuscript directly.

6. Supporting Editorial Administration

Publishers may use approved AI-based systems to assist with manuscript screening, completeness checks, identifying potential reviewers, or retrieving relevant information.

Such tools operate within publisher-controlled workflows and governance arrangements.

They should not be confused with unrestricted reviewer use of public generative AI services.

Editorial decisions still require human responsibility and oversight.

AI-Assisted Peer Review vs. Traditional Peer Review

Traditional peer review depends on experts assessing a manuscript's scientific quality.

AI-assisted peer review retains that essential requirement but introduces software support for limited tasks.

The purpose of AI-assisted reviewing is not to automate acceptance or rejection.

It is to support specific tasks without undermining the independence of expert assessment.

What Do Major Publishers Say About AI in Peer Review?

Publisher policies are particularly important because different journals permit different levels of assistance.

Springer Nature's AI Peer Review Policy

Springer Nature uses a risk-assessment framework for AI assistance.

Its guidance distinguishes lower-risk activities from uses that could compromise independent judgment or confidentiality.

Examples of lower-risk support include improving review clarity, organizing suitable notes, and preparing general reporting checklists.

More sensitive uses include exploring potential methodological concerns or using AI to challenge a reviewer's reasoning.

These activities require particular care, independent verification, and compliance with confidentiality restrictions.

Springer Nature does not permit reviewers to delegate their critique, generate a complete review report through AI, or use AI to determine an accept-or-reject recommendation.

Uploading confidential manuscript content into public or unsecured systems is also prohibited.

The publisher emphasizes that human scholarly judgment and accountability cannot be transferred to AI.

Elsevier's Generative AI Policy for Reviewers

Elsevier's June 2026 policy states that reviewers must not upload a submitted manuscript or any part of it into an AI tool.

The restriction protects unpublished research, confidentiality, intellectual property, and potentially personal information.

Elsevier allows limited supportive uses that preserve confidentiality and human oversight.

Examples include improving the language or organization of a reviewer report and conducting general background literature searches.

Its reviewer guidance also discusses disclosure when AI is used to improve review language or structure.

Basic spelling and grammar checks may be treated differently from substantive generative assistance.

Reviewers should consult the journal's latest instructions before using a particular tool.

Wiley's AI Peer Review Policy

Wiley requires reviewers to maintain the confidentiality of submitted manuscripts and peer-review correspondence.

Reviewers must not upload manuscript text, figures, tables, or other confidential material into generative AI systems.

Wiley permits limited AI assistance with written review feedback, provided the reviewer retains responsibility and makes required disclosures.

Individual Wiley journals may publish more specific instructions.

For example, some journals restrict generative AI in review writing while allowing declared assistance with translation or readability.

Therefore, reviewers should follow the policy of the journal handling the manuscript, not assume that one publisher-wide statement authorizes every possible use.

What Are the Benefits of AI-Assisted Peer Review?

AI may offer useful support when employed within appropriate ethical and editorial boundaries.

1. Clearer Reviewer Feedback

Language assistance can help reviewers communicate their concerns more precisely.

This may be especially useful for academics writing reviews in a language other than their first language.

However, the tool must not change the scientific meaning of the comments.

2. Better Organization of Reports

Structured feedback can help authors understand the main issues requiring attention.

AI may assist with permitted formatting or organization tasks.

3. Easier Access to General Methodological Guidance

AI-supported literature discovery can help reviewers locate relevant public resources and research standards.

All scientific information still requires verification.

4. Support for Interdisciplinary Orientation

Researchers reviewing interdisciplinary work may need background information on methods outside their primary specialization.

AI can support general learning, but it cannot replace expertise in areas essential to evaluating the manuscript.

5. Reduced Administrative Effort

Publisher-approved AI systems may help editors manage submission screening, reviewer identification, and similar tasks.

This could reduce some administrative burdens.

6. More Consistent Presentation

AI-based language tools may help reviewers improve readability, terminology, and report structure.

These benefits are primarily potential advantages. They should not be interpreted as proof that AI-assisted reviews are universally faster, fairer, or more scientifically accurate.

What Are the Risks of AI-Assisted Peer Review?

AI introduces several risks that require careful management.

1. Manuscript Confidentiality Breaches

A submitted research manuscript is usually confidential.

Uploading it to an external AI tool may disclose unpublished methods, findings, figures, or other protected information.

A tool advertised as private is not automatically approved for peer review.

Reviewers must verify both the tool's protections and the journal's requirements.

2. Hallucinated Scientific Information

Generative AI systems can produce explanations that sound authoritative but are incorrect.

They may also generate nonexistent academic references or misrepresent published studies.

A reviewer who relies on such outputs without verification may introduce errors into an assessment.

3. Inaccurate Methodological Criticism

AI may recommend statistical analyses that do not fit the research design.

For example, it might suggest a simple independent-samples test when observations are repeated or clustered.

This could lead to inappropriate reviewer requests.

4. Automation Bias

Automation bias occurs when people give excessive weight to a computer-generated suggestion.

A reviewer might accept an AI-generated criticism without sufficiently evaluating whether it applies to the manuscript.

5. Reinforcement of Existing Bias

AI systems may reflect limitations or patterns present in their training data.

Outputs could reproduce assumptions about established theories, research traditions, institutions, or methodological approaches.

This makes independent human assessment essential.

6. Weak Reviewer Accountability

If AI generates the scientific critique, it may become unclear whether the invited reviewer has exercised the required expertise.

Publisher policies generally place responsibility on the human reviewer.

7. Unclear Disclosure

Undeclared AI use can make it difficult for editors to determine how a report was prepared.

Policies concerning disclosure differ, so reviewers should check the applicable instructions.

8. False Confidence in AI Detection

Tools claiming to identify AI-generated writing are not necessarily reliable enough to establish misconduct.

Reviewers should not accuse authors of inappropriate AI use based solely on a detection score.

Concerns should be supported by verifiable evidence and referred to the editor when appropriate.

Can You Upload a Research Manuscript to ChatGPT for Peer Review?

For a confidential manuscript received through a journal's peer-review process, the answer is generally no unless the journal explicitly authorizes an appropriate workflow.

Elsevier and Wiley prohibit reviewers from uploading submitted manuscript content into generative AI tools.

Springer Nature prohibits uploading confidential material to public or unsecured AI systems and allows only carefully controlled uses under its policies.

The restrictions are not limited to uploading an entire PDF.

Copying an unpublished abstract, figure, dataset, or manuscript passage into a prohibited system can also breach confidentiality.

Reviewers should not assume that removing the author's name makes disclosure acceptable.

Unpublished scientific findings and distinctive research methods may remain identifiable and confidential.

The safer approach is to conduct the scientific review personally and seek editorial authorization before using any tool involving protected manuscript information.

Can AI Write a Peer Review Report?

AI can generate text resembling a reviewer report, but that does not make it an appropriate substitute for expert peer review.

A scientific review requires understanding the research question, design, relevant literature, analysis, limitations, and implications.

AI-generated reports may contain generic criticism that sounds plausible without accurately identifying weaknesses in the actual study.

More importantly, major publisher policies require reviewers to exercise independent judgment.

Springer Nature explicitly prohibits delegating critique or generating full review reports through AI.

Elsevier emphasizes that AI cannot replace the reviewer's critical thinking or scientific responsibility.

Reviewers should therefore develop their own evaluation and use permitted assistance only for supporting tasks.

Practical Example: Responsible AI Assistance During Peer Review

Consider a hypothetical reviewer evaluating an electrical engineering manuscript about fault detection in smart power grids.

The reviewer independently examines the paper's experimental design, performance metrics, validation procedures, and conclusions.

During evaluation, the reviewer identifies three concerns:

1. The comparison dataset may not represent all operating conditions.
2. The performance metrics require clearer reporting.
3. Some conclusions appear broader than the experiments support.

The reviewer prepares an original draft based on these observations.

If the journal permits suitable AI language assistance, the reviewer may use an approved workflow to improve the clarity and organization of the report.

The reviewer must not upload confidential manuscript details into a prohibited system.

The revised report remains subject to the reviewer's own scientific verification.

If the journal requires disclosure, the reviewer identifies the tool and its limited role.

The editor then receives a report grounded in the reviewer's expertise rather than AI-generated scientific judgment.

Example of Appropriate and Inappropriate AI Use

The exact policy matters more than whether a particular product is generally described as safe or private.

How Should Reviewers Disclose AI Use?

When a journal permits AI assistance and requires disclosure, the reviewer should describe the tool and its purpose.

Some publishers ask for the name of the tool, the nature of the assistance, and confirmation that the reviewer checked the final report.

A suitable disclosure for a permitted language-editing workflow might be:

"During preparation of this review report, I used [Tool Name] to improve the clarity and organization of text I had independently written. I verified the final wording and remain fully responsible for the scientific assessment and comments."

This declaration should only be used if the described activity actually occurred and the journal permits it.

It does not authorize uploading confidential manuscript material.

Reviewers should also follow any required disclosure location, such as a confidential note to the handling editor.

Ethical Guidelines for AI-Assisted Peer Review

Reviewers can follow a practical set of principles when evaluating AI assistance.

Check the Journal's Policy First

Do not assume that permission from one journal applies to another.

Read the current instructions before using an AI tool.

Protect Unpublished Research

Do not disclose confidential text, figures, data, or identifiable findings to unauthorized systems.

Keep Scientific Judgment Human-Led

Evaluate the manuscript personally.

AI-generated suggestions must never replace independent expert reasoning.

Verify Every Scientific Claim

Check references, methodological information, and any facts suggested by AI.

Use the Minimum Necessary Information

When permitted to consult AI for general methodological support, use broad questions that do not reveal manuscript-specific information.

Disclose AI Assistance Appropriately

Follow the journal's requirements for reporting AI use.

Avoid Unsupported Accusations

Do not accuse authors of misconduct simply because a detection tool reports an elevated AI-generated-text probability.

Maintain Reviewer Accountability

The invited reviewer remains responsible for the complete submitted report.

These principles align with the emphasis on independence, confidentiality, transparency, and accountability in major publishers' current policies.

What Is the Difference Between AI-Assisted and AI-Generated Peer Review?

An AI-assisted review is a scientifically independent human review that includes limited, authorized tool support.

An AI-generated review relies on an AI system to produce substantive manuscript criticism or editorial recommendations.

This distinction is important because fluent text is not the same as expert scholarly evaluation.

For example, improving the structure of independently written comments may qualify as assistance.

In contrast, asking a chatbot to produce the strengths, weaknesses, and final recommendation for an unpublished manuscript delegates the essential reviewing task.

Publishers increasingly distinguish between these uses in their editorial policies.

Can AI Replace Human Peer Reviewers?

AI is not an adequate replacement for human peer reviewers under the current policies examined.

Manuscript assessment requires contextual scientific judgment, awareness of disciplinary standards, and responsibility for the resulting critique.

AI may help with approved editorial tasks, but it can make mistakes, overlook important limitations, and generate unsupported conclusions.

Springer Nature, Elsevier, and Wiley all maintain that reviewers and editors must remain accountable for scholarly evaluation.

The appropriate direction is therefore carefully governed human-led review supported by useful technologies—not automatic replacement of expert judgment.

What Is the Future of AI in Peer Review?

AI is likely to remain relevant to academic publishing as tools and governance practices evolve.

Potential applications include improved research discovery, more efficient editorial screening, structured reporting assistance, and support for detecting inconsistencies requiring human investigation.

However, future systems will need to address confidentiality, data retention, bias, transparency, reliability, and accountability.

Publisher-approved systems may provide greater protection than unrestricted external services, but those protections still require verification.

The central challenge will be determining where automated assistance genuinely improves peer review without compromising its independence.

Scientific judgment, research integrity, and editorial responsibility should remain the standards against which new applications are assessed.

Frequently Asked Questions About AI-Assisted Peer Review

What is AI-assisted peer review?

AI-assisted peer review is the limited use of artificial intelligence to support selected manuscript-review activities while keeping scientific evaluation and responsibility with the human reviewer.

Can reviewers use ChatGPT during peer review?

It depends on the journal's policy and the intended use. Major publishers restrict disclosure of confidential manuscript content to AI tools. Some permit limited assistance with language or general background questions.

Can I upload an unpublished manuscript to an AI tool?

Reviewers should not upload confidential manuscripts into public or unauthorized AI systems. Several major publishers explicitly prohibit this practice.

Can AI generate reviewer comments?

AI can generate text, but reviewers should not delegate their substantive scientific critique to it. Publisher policies may prohibit AI-generated reports and recommendations.

What are the benefits of AI-assisted peer review?

Potential benefits include clearer written feedback, better organization, general methodological support, and more efficient editorial administration when tools are used appropriately.

What are the risks of using AI in peer review?

Major risks include confidentiality breaches, fabricated references, inaccurate criticism, automation bias, inappropriate delegation, and inadequate disclosure.

Do reviewers have to disclose AI use?

Many journals require disclosure of substantive AI assistance. Requirements vary for basic grammar correction, language editing, and other uses. Reviewers should check the specific journal policy.

Can AI recommend accepting or rejecting a manuscript?

AI may generate such recommendations, but publishers generally do not permit reviewers or editors to delegate publication judgments to AI systems.

Is a private AI tool safe for confidential peer review?

Not automatically. Data retention, access controls, training policies, contractual protections, and journal authorization all matter.

Will AI replace academic peer reviewers?

Current major-publisher policies require human accountability and independent scholarly evaluation. AI may support certain tasks, but it should not replace expert reviewers.

Conclusion

AI-assisted peer review introduces opportunities to improve communication, organization, and certain supporting activities in academic manuscript evaluation.

However, these potential benefits must be balanced against significant risks involving confidentiality, accuracy, bias, and reviewer accountability.

Major publishers have established policies that prohibit or strictly limit uploading unpublished manuscripts into AI systems and require scientific judgments to remain human-led.

Reviewers should read the journal's specific policy, protect confidential research, verify AI-generated information, and disclose permitted assistance when required.

The most responsible approach is to use AI as a carefully controlled supporting technology rather than a substitute for scholarly expertise.

Peer review depends on independent reasoning, professional integrity, and accountability—principles that remain essential regardless of the tools available.