AI in Peer Review: Enhancing Accuracy, Reducing Bias, and Improving Efficiency

How AI Is Enhancing the Peer Review Process: Opportunities, Risks, and Best Practices

Introduction

Peer review is often described as the backbone of scholarly publishing. Before research appears in journals, books, or conference proceedings, it is evaluated by experts who check whether the work is original, methodologically sound, ethically conducted, and relevant to the field. This process is central to maintaining trust in the scientific record.

However, the traditional peer review system is under serious strain. Journals receive more submissions than ever before, while the number of qualified reviewers willing to volunteer their time has not increased at the same pace. As a result, editors struggle to find reviewers, review times lengthen, and concerns about bias, inconsistency, and missed errors or misconduct persist.

In this context, Artificial Intelligence (AI) is emerging as a powerful ally. AI cannot replicate the nuanced judgement of an experienced researcher, but it can help with initial screening, plagiarism and image checks, statistical validation, reviewer selection, and even analysis of review tone and fairness. Used carefully, AI has the potential to make peer review more efficient, more consistent, and more transparent, while allowing human reviewers to focus on the aspects of research that require deep expertise.

This article examines how AI is currently being used to enhance peer review, the benefits it offers, the ethical and technical challenges it poses, and how publishers and researchers can integrate AI responsibly while preserving the integrity of academic evaluation.

Challenges in the Traditional Peer Review Process

Before considering how AI can help, it is useful to outline the main problems that plague the current system.

1. Time-Consuming Workflows

Conventional peer review can take weeks or months. Editors must scan submissions, identify suitable reviewers, send invitations, chase responses, and manage multiple rounds of revision. For authors, this can mean long delays before their work is publicly available, even when the research is time-sensitive.

2. Reviewer Fatigue and Shortage

The workload placed on reviewers has become unsustainable in many fields. Busy academics juggle teaching, grant applications, supervision, their own research, and sometimes administrative duties. Review requests often arrive on top of all this, and many scholars now decline more reviews than they accept. Those who do say yes may be overwhelmed, leading to slower or less thorough evaluations.

3. Subjective and Inconsistent Evaluations

Human judgement is invaluable but also imperfect. Reviewers may disagree strongly with one another or apply very different standards to similar manuscripts. Personal preferences, theoretical alignments, or unconscious biases can influence decisions. As a result, some high-quality papers are rejected, while weaker work may occasionally slip through.

4. Limited Detection of Misconduct

Plagiarism, image manipulation, and data fabrication are relatively rare but serious threats to research integrity. Detecting them by hand is extremely difficult. Reviewers generally do not have time to cross-check every sentence or figure against the entire published literature, and sophisticated fraud can be carefully concealed.

5. Inefficient Reviewer Matching

Choosing the right reviewers is crucial. Editors must identify people with the right subject expertise, methodological skills and independence (i.e. no conflicts of interest), but traditional tools for doing so are limited. As a result, reviewers may be selected who are only marginally familiar with a topic, leading to shallow or misdirected feedback.

These challenges have motivated journals and publishers to explore whether AI can help support a more efficient, fair, and robust peer review system.

How AI Is Enhancing Peer Review

AI is not a single technology but a collection of methods—machine learning, NLP, pattern recognition, anomaly detection—that can be applied at different stages of the editorial workflow. Below are key areas where AI is already having an impact.

1. AI-Assisted Initial Screening

Initial screening is a natural place to start. Many journals receive far more submissions than they can reasonably send out for full review. AI tools can help editors triage manuscripts before they reach human reviewers.

• Technical checks: AI can verify that manuscripts meet basic formatting requirements, include mandatory sections (e.g. methods, ethics statements), and comply with word or figure limits.
• Scope assessment: NLP models can compare the manuscript’s content with the journal’s scope, highlighting obviously off-topic submissions.
• Quality signals: Tools such as StatReviewer or SciScore can assess reporting completeness (e.g. CONSORT or ARRIVE items), flag missing ethical approvals, or identify superficial methodological descriptions.

Impact: Editors spend less time on administrative screening, and only manuscripts that pass basic quality and scope checks are forwarded to human reviewers.

2. AI for Plagiarism and Image Manipulation Detection

AI-based similarity and image-forensics tools now play a central role in many editorial offices.

• Plagiarism detection: Tools like iThenticate and Turnitin compare the manuscript against large databases of articles, theses and web pages, highlighting overlapping text and potential self-plagiarism.
• Image analysis: Software such as Proofig can detect duplicated panels, cloned regions, or suspicious manipulations in figures, even when they have been transformed or re-labelled.

Impact: Research integrity is strengthened, and journals can identify a significant proportion of misconduct or sloppy practice before publication, reducing the risk of retractions later.

3. AI-Driven Reviewer Selection

AI can assist editors in selecting reviewers who are suitably qualified and independent.

• Expertise matching: Tools like Elsevier’s Reviewer Finder analyse keywords, abstracts and reference lists and compare them with researcher profiles and publication histories to suggest potential reviewers with relevant expertise.
• Conflict detection: AI can examine co-authorship networks and institutional affiliations to identify potential conflicts of interest (e.g. recent collaborators or same-department colleagues).

Impact: Reviewer matching becomes faster, fairer, and more targeted, increasing the likelihood of thoughtful, expert evaluation.

4. AI-Powered Sentiment and Bias Detection

Once reviews are submitted, AI can analyse the text to assess tone and potential bias.

• Sentiment analysis: NLP models can identify reviews that are unusually harsh, vague, or overly positive without justification.
• Bias indicators: Systems can flag language that appears personal, discriminatory, or irrelevant to the scientific content.
• Review quality feedback: Some tools can suggest ways to rephrase comments to make them more constructive and specific.

Impact: Editors gain additional information about the fairness and professionalism of reviews and can discount or query feedback that appears biased or unhelpful.

5. AI-Assisted Statistical and Methodological Validation

Many papers involve complex statistics or specialised methods that not every reviewer is comfortable evaluating in depth. AI can provide a second line of defence.

• Statistical checks: Tools like StatCheck in psychology compare reported p-values with test statistics and degrees of freedom to detect inconsistencies.
• Methodology patterns: AI can flag unusual effect sizes, improbable data distributions, or problematic experimental designs relative to norms in the field.

Impact: Statistical errors and questionable practices are more likely to be spotted, supporting more robust and trustworthy conclusions.

6. AI for Language and Readability Improvements

Clarity of language is not a trivial matter: poorly written manuscripts are harder to evaluate and more likely to be misunderstood. AI-powered writing tools can help authors improve readability before submission.

• Tools like Grammarly or Trinka AI detect grammatical errors, awkward phrasing, and issues with academic tone.
• Machine translation and language-support tools help non-native English speakers express their ideas more clearly.

Impact: Reviewers can focus on the scientific substance rather than being distracted by language issues. However, given that many institutions prohibit AI-generated text, authors should limit such tools to local corrections and use professional human proofreading for major revisions to avoid similarity and policy problems.

Ethical and Practical Concerns of AI in Peer Review

Despite its benefits, the use of AI in peer review raises important questions that must be addressed to maintain trust and fairness.

1. Algorithmic Bias

AI systems learn from data; if the data are biased, so are the models. This can manifest as:

• Preference for topics, methods or institutions that are common in the training set, potentially disadvantaging emerging areas or under-resourced regions.
• Over-reliance on citation metrics or journal prestige, reinforcing existing inequalities rather than focusing on intrinsic quality.

Mitigating bias requires diverse training data, regular auditing, and transparency about how AI tools make recommendations.

2. Lack of Human Judgement in Complex Evaluations

AI can check structure, statistics and surface features, but it cannot truly assess:

• The novelty of an idea in the context of a field’s history and ongoing debates.
• The theoretical contribution that a new conceptual framework might make.
• The creative or interdisciplinary leap that an unconventional method or question represents.

These assessments require human judgement, tacit knowledge, and often a sense of scholarly “taste” that cannot be coded into an algorithm.

3. Data Privacy and Confidentiality

Peer review operates on unpublished manuscripts that are usually confidential. Integrating AI introduces questions such as:

• Where are manuscripts processed and stored when analysed by AI tools?
• Are texts or figures used to train models without authors’ consent?
• How are journals ensuring compliance with regulations such as GDPR or HIPAA when medical or personal data are involved?

Journals must ensure that AI tools are embedded within secure infrastructures and that authors are informed about how their submissions are processed.

4. Over-Reliance on AI Outputs

AI results can appear definitive when presented as scores or red-flag lists. But AI is not infallible:

• Editors may be tempted to follow AI recommendations mechanically rather than applying their own judgement.
• Reviewers might assume that “the AI has already checked for problems” and be less vigilant.
• Important but subtle issues that fall outside AI’s detection capabilities might be overlooked.

For this reason, AI should be clearly framed as a support tool, with final decisions always resting with human editors and reviewers.

The Future of AI-Enhanced Peer Review

Looking ahead, AI’s role in peer review is likely to grow—but in a supportive, not dominant, capacity.

• Hybrid AI–human models: AI conducts initial checks and triage; human experts lead detailed evaluation and final decisions.
• More advanced NLP models: Future tools may better understand argument structure and could generate more targeted questions for reviewers rather than generic feedback.
• Bias-monitoring dashboards: AI could be used to detect patterns in editorial decisions and review reports that suggest systemic bias, prompting corrective action.
• Integration with open science: As more data, code and protocols are shared openly, AI will have richer material to use when verifying methods and results.
• Blockchain and provenance tracking: Combined with AI, blockchain-based systems may allow more transparent tracking of review histories and version changes.

Best Practices for Using AI Responsibly in Peer Review

To harness the benefits of AI while avoiding its pitfalls, publishers and researchers can adopt a set of practical guidelines.

• Define clear roles: Specify which tasks AI will handle (e.g. plagiarism checks, reviewer suggestions) and where human judgement is mandatory.
• Maintain transparency: Inform authors and reviewers when AI tools are used and, where possible, provide interpretable outputs rather than opaque scores.
• Prioritise security: Ensure that all AI processing occurs in secure, compliant environments and that manuscripts are not shared with third-party tools without consent.
• Monitor performance and bias: Regularly audit AI recommendations against human decisions and outcomes to detect unwanted patterns.
• Train editors and reviewers: Provide guidance on how to interpret AI outputs and how to balance them with their own expertise.

Implications for Authors and the Role of Human Proofreading

For authors, the rise of AI in peer review has two key implications:

• Manuscripts are likely to face more rigorous automated checks for similarity, statistics, ethics and structure. Sloppy or non-compliant submissions will be detected more quickly.
• Universities and publishers are increasingly strict about AI-generated text. Many now require authors to declare any use of generative AI and treat undisclosed AI writing as a breach of integrity.

Given this environment, the safest strategy is to keep the intellectual content and wording of your manuscript human-written and to use AI tools, if at all, only for internal drafting or idea exploration—not for producing submission-ready prose. For language quality, clarity, and journal-specific style, professional human proofreading and editing remain the most reliable option. Human proofreaders can improve grammar, structure and readability without increasing similarity scores or violating AI-use policies, and they can also ensure that your manuscript meets the expectations of peer reviewers and editors.

Conclusion

AI is already reshaping the peer review landscape. By assisting with initial screening, plagiarism and image detection, reviewer selection, bias analysis, statistical checks and language improvement, AI tools can make peer review faster, more consistent and more robust. At the same time, AI has clear limitations: it lacks deep subject understanding, may reproduce biases present in training data, and raises important questions about data privacy and over-reliance on automation.

The future of peer review is therefore not AI versus humans but AI with humans. A hybrid model—where AI handles repetitive and large-scale tasks and human reviewers provide contextual, ethical and theoretical judgement—offers the best of both worlds. When combined with clear ethical guidelines, secure infrastructures, and high-quality human proofreading for authors, AI-assisted peer review can help create a system that is faster, fairer and more transparent, while preserving the core values of scholarly evaluation.