Summary
Peer review is essential for ensuring that academic and scientific articles meet acceptable quality standards before publication, but finding the right reviewers is difficult and time-consuming. Editors must identify experts with appropriate subject knowledge, check for conflicts of interest, and hope that they are available and willing to review. Traditional methods – manual searches, personal networks, and ad hoc queries – struggle to cope with the volume and diversity of modern submissions, creating delays and uneven workloads.
This article explains how artificial intelligence (AI) is transforming the way journals select and manage reviewers. It describes how AI tools analyse manuscripts, publication data, and collaboration networks to match submissions with qualified, unbiased reviewers; how they help detect conflicts of interest and predict reviewer availability; and how they can monitor performance over time to support more consistent, constructive reviews. The article also discusses the advantages of AI-assisted matching – greater efficiency, reduced reviewer fatigue, improved fairness – alongside challenges such as data privacy, algorithmic bias, and the danger of over-reliance on automated recommendations.
Finally, the article outlines ethical and practical guidelines for using AI responsibly in editorial workflows and sketches possible future developments, including hybrid AI–human models and diversity-aware matching. Throughout, it emphasises that AI should support, not replace, editorial judgement, and that clear, carefully edited communication remains vital. Human academic proofreading is still the safest option for ensuring that editor and publisher documentation about AI use is precise, transparent, and compliant with institutional and regulatory expectations.
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How AI Is Optimizing Peer Reviewer Selection in Scholarly Publishing
Introduction: Peer Review Under Pressure
Peer review lies at the heart of academic publishing. Before a manuscript is accepted, it is normally evaluated by one or more experts who assess its originality, methods, analysis, and contribution to the field. In principle, this process protects research quality and helps authors improve their work. In practice, however, one step in the process often proves difficult and time-consuming: finding suitable reviewers.
Editors are expected to identify reviewers who:
- have the right expertise for the manuscript’s topic and methods,
- are not in conflict with the authors,
- are reliable and constructive, and
- are available within the desired timeframe.
Traditionally, reviewer selection has relied on personal editorial networks, manual searches in databases, and suggestions from authors. This approach may work reasonably well for small, specialised journals, but as submission volumes grow, it becomes increasingly inefficient and uncertain. Editors spend large amounts of time sending invitations that are declined or ignored, while the same small group of “usual suspects” is overburdened with requests and early-career experts remain invisible.
Advances in artificial intelligence (AI) and data analytics now offer an alternative. By analysing publication records, keywords, citation networks, and past reviewing behaviour, AI-powered tools can help editors find and select reviewers more quickly and systematically. Used carefully, these systems promise to make peer review faster, fairer, and more transparent – while still keeping humans in control.
The Challenges of Traditional Reviewer Selection
Before exploring how AI can help, it is important to clarify the problems that editors currently face.
Limited availability and reviewer fatigue
Many active researchers receive multiple review requests each week. Since reviewing is often unpaid and must fit around teaching, research, and administration, many invitations are declined or accepted with significant delay. Editors may send dozens of invitations before securing two or three reviewers, especially in highly specialised or rapidly developing areas.
Matching expertise and avoiding bias
Selecting a reviewer is not simply a matter of finding someone who works in a vaguely related field. Editors must ensure that reviewers:
- have detailed knowledge of the manuscript’s specific topic and methods, and
- do not have strong personal or professional ties to the authors that could bias their judgement.
Manual searches through databases such as PubMed, Scopus, or Web of Science can identify potential experts, but evaluating their suitability is labour-intensive. Editors may also consciously or unconsciously rely on familiar names in their own networks, which can introduce geographic, institutional, or demographic bias.
Conflicts of interest
Conflicts of interest can arise when potential reviewers:
- work at the same institution as the authors,
- have recently co-authored articles with them,
- are in direct competition for funding or visibility, or
- have personal relationships with the authors.
Investigating these relationships manually is difficult and often incomplete, especially when authors and reviewers have complex collaboration histories across multiple institutions.
Time-consuming, uneven processes
Because the traditional approach depends heavily on individual editors’ knowledge and available time, it is inherently uneven. Some manuscripts move quickly because the editor happens to know suitable reviewers; others linger for weeks because the editor must start from scratch. This inconsistency frustrates authors and can damage a journal’s reputation.
How AI Is Transforming Reviewer Matching
AI-assisted reviewer selection systems aim to address these challenges by analysing large volumes of structured and unstructured data far more quickly than any human can. While specific tools differ in their algorithms and interfaces, most follow a similar logic.
1. Expertise matching through text and metadata analysis
When a manuscript is submitted, AI tools can read its title, abstract, keywords, and references to build a profile of its subject matter and methods. Techniques from natural language processing (NLP) and machine learning then compare this profile with those of millions of published articles.
Potential reviewers are identified based on:
- topics they have published on,
- methods and techniques they frequently use, and
- the recency and relevance of their work.
For example, a manuscript on “deep learning for detecting diabetic retinopathy” might be matched with reviewers who have recent publications in both medical image analysis and deep neural networks, rather than with any ophthalmologist or any machine-learning researcher. This fine-grained matching is difficult to perform manually but relatively straightforward for AI systems once trained on large corpora of articles.
2. Automated conflict-of-interest detection
AI tools can also check for potential conflicts of interest by analysing:
- author and reviewer affiliations (current and past),
- co-authorship networks,
- joint funding acknowledgements, and
- membership in the same research consortia or committees.
By cross-referencing this information, AI systems can flag candidates who have recently co-authored with the authors, work in the same department, or have other close connections. Editors can then decide whether to exclude these reviewers, reducing the risk of biased or perceived-biased evaluations.
3. Predicting reviewer availability and responsiveness
An AI system can examine past reviewing behaviour to estimate whether a particular candidate is likely to accept a new assignment and deliver it on time. Relevant signals include:
- the proportion of past invitations they accepted or declined,
- average review completion time,
- recent publication activity (very active authors may be busier), and
- seasonal patterns (some reviewers are less available at certain times of year).
While these predictions are never perfect, they allow editors to prioritise invitations to reviewers with a high probability of acceptance and timely completion, speeding up the process and reducing the number of “cold” invitations sent.
4. Assessing review quality and reliability
Some AI systems also analyse past review reports (where available) to assess:
- whether reviews are detailed or superficial,
- whether feedback is balanced and constructive, and
- whether reviewers’ recommendations align reasonably with editorial decisions.
This information helps editors distinguish between reviewers who consistently provide thoughtful, well-structured feedback and those whose comments are minimal, late, or problematic. Over time, such monitoring can encourage higher standards and discourage unreliable reviewing practices.
5. Continuous improvement through machine learning
Modern reviewer-matching platforms often incorporate editorial feedback to refine their recommendations. For example, editors can rate the suitability of suggested reviewers, indicate whether invitations were accepted or declined, and flag conflicts missed by the system. Machine-learning models use this feedback to improve future predictions, gradually tailoring the matching process to the specific needs and preferences of each journal.
Advantages of AI-Assisted Reviewer Selection
Used thoughtfully, AI offers several significant benefits for journals, editors, reviewers, and authors.
1. Efficiency and speed
AI systems can scan vast databases and produce a ranked list of potential reviewers in seconds, dramatically reducing the time editors spend on manual searches. This efficiency:
- shortens the initial stage of the peer-review process,
- allows editors to focus on content and decisions rather than logistics, and
- can make a journal more attractive to authors who value quick responses.
2. Better workload distribution and reduced reviewer fatigue
Because AI tools can access large pools of potential reviewers, they are well placed to identify under-utilised experts, including early-career researchers whose publication records demonstrate expertise but who may not yet appear in editors’ personal networks. Broadening the reviewer base:
- shares the reviewing burden more fairly,
- reduces pressure on a small number of “go-to” reviewers, and
- creates new opportunities for emerging scholars to contribute.
3. Increased objectivity and diversity
While no system is completely free from bias, AI-assisted matching can reduce some forms of human bias by focusing on data (publication records, expertise, performance) rather than familiarity or reputation. When combined with explicit editorial policies, AI tools can help:
- promote geographic, institutional, and gender diversity in reviewer pools,
- ensure that specialised subfields are adequately covered, and
- minimise unconscious preferences for certain universities or regions.
4. Systematic conflict-of-interest management
By systematically scanning affiliation and collaboration networks, AI tools can catch conflicts of interest that busy editors might miss, especially when relationships span multiple institutions or involve large consortia. This strengthens the integrity of the review process and helps journals demonstrate due diligence if disputes arise.
5. Potential improvements in review quality
By tracking reviewer performance and prioritising those who are reliable, thorough, and constructive, AI-assisted systems can gradually raise the overall quality of peer review. Editors can build a more nuanced picture of their reviewer community and recognise those who contribute consistently high-value feedback.
Challenges and Ethical Considerations
Despite these advantages, there are significant challenges and ethical questions associated with AI in reviewer selection. Journals must address these issues to ensure that technological gains do not come at the cost of fairness, transparency, or trust.
1. Data privacy and regulation
AI-based tools often rely on detailed information about researchers’ publications, affiliations, and reviewing histories. While much of this data is public, some is not. Journals and service providers must:
- comply with data protection regulations such as GDPR,
- make clear to reviewers how their data are being used, and
- ensure that data are stored securely and not shared beyond agreed purposes.
2. Algorithmic bias and transparency
AI systems learn from historical data. If past reviewer selection patterns were biased – for example, favouring well-known institutions or established researchers – those biases can be encoded and amplified by the algorithm. To mitigate this risk:
- developers and journals should monitor outputs for systematic patterns (e.g. under-representation of certain regions or career stages);
- adjustments can be made to deliberately broaden reviewer pools; and
- where possible, decision criteria should be documented so that humans can understand and challenge AI recommendations.
3. Over-reliance on automation
AI tools should be seen as decision support, not decision makers. Editorial judgement remains crucial for:
- assessing nuanced expertise that is not fully captured by publication records,
- considering sensitive interpersonal or reputational factors, and
- balancing competing priorities such as speed, depth, and fairness.
Editors should feel free to override AI suggestions when they have good reasons to do so, and they should review automatic decisions periodically to ensure they align with journal values.
4. Communication and trust
Authors and reviewers may be wary of “black-box” systems that make invisible choices. Clear communication about:
- what AI tools are used,
- what data they rely on, and
- how final decisions are made
helps maintain trust. Publicly available editorial policies and carefully written guidance – reviewed and polished by experienced human proofreaders – can play an important role in building confidence.
The Future of AI-Assisted Reviewer Selection
The use of AI in reviewer matching is still evolving. In the coming years, we are likely to see:
- Hybrid AI–human systems in which tools generate suggestions and flag conflicts, but editors retain full control of final assignments.
- Diversity-aware algorithms that explicitly take geographic, institutional, or demographic representation into account to build more inclusive reviewer panels.
- Improved content understanding through advances in natural language processing, enabling tools to capture subtle nuances of methodology and theory when matching expertise.
- Integrated editorial dashboards that combine reviewer matching, tracking, performance metrics, and workload management into a single interface.
As these technologies become more sophisticated and more widely adopted, editorial teams will need ongoing training and clear policy frameworks to ensure that efficiency gains are balanced with ethical, transparent practice.
Conclusion: AI as a Partner, Not a Replacement
AI-assisted reviewer selection offers a powerful response to some of the most persistent challenges in peer review: identifying appropriate experts, managing conflicts of interest, reducing delays, and avoiding reviewer fatigue. By harnessing large-scale data and advanced analytics, these tools can help editors find qualified reviewers more quickly and distribute work more fairly across the research community.
However, AI is not a cure-all and must be implemented with care. Issues of data privacy, algorithmic bias, over-reliance on automation, and the need for transparency cannot be ignored. The most effective model is a partnership: AI tools provide evidence-based suggestions and alerts, while human editors apply their knowledge, experience, and ethical judgement to make final decisions.
For journals and publishers, this partnership extends to how they communicate about AI use. Clear, well-crafted documentation, policies, and author guidelines – refined through professional human proofreading – are essential for maintaining trust in the peer-review process. As AI continues to evolve, the goal should not be to replace human expertise, but to support it, helping build a peer-review system that is faster, more reliable, and more equitable for authors, reviewers, and editors alike.
