Ask most people what they think of peer review, and you’ll hear a mix of respect and realism. It sits at the very heart of academic publishing – a powerful way to uphold rigour and advance knowledge. But it’s also demanding. Authors may wait months for feedback. Reviewers tackle intricate analyses alongside their own research. Editors juggle timelines, priorities, and the delicate task of guiding manuscripts through fair, thorough assessment. It’s a system that works, yet everyone recognises it carries pressures and imperfections.
So, it’s little wonder that artificial intelligence (AI) has stepped in. Publishers and journals across the globe are investing heavily in AI-driven tools to alleviate the burden. But these technologies aren’t replacing human decision-making. They’re mostly about smart support – streamlining manual tasks, highlighting potential issues faster, and freeing up editorial teams to focus on what only people can do: interpret, judge, and steer the scholarly conversation.
It’s tempting to think technology might finally solve all the headaches, but the truth is more interesting. Here’s how AI is helping, where it stops short, and why we still need sharp human minds to steer the process.
Screening smarter
The first place AI tends to show up is at the front gate: initial screening. Before peer review even starts, most reputable journals run submissions through a series of checks. AI now powers much of this triage.
- Plagiarism detection is perhaps the best known. Tools like Turnitin, iThenticate, and Crossref Similarity Check scan new manuscripts against vast databases of published work and web content to flag suspicious overlaps. While plagiarism software isn’t new, machine learning continues to improve the detection of nuanced similarities and disguised rewordings, catching issues that might slip past even eagle-eyed human editors.
- Language quality checks are another growing frontier. AI systems assess grammar, clarity, and coherence, sometimes flagging manuscripts that may need pre-review language polishing. This spares reviewers from wading through prose that’s too rough to judge on merit alone.
- Technical compliance screening – verifying that figures are of high enough resolution, that all supplementary data files are present, and that references meet journal style – is increasingly automated. It’s a quiet revolution that saves editorial staff hours per week. Some systems even automatically check for key ethical statements, like human subjects’ approvals or data availability declarations, flagging gaps before they become awkward retractions later on.
By tackling these mechanical checks, AI ensures humans don’t waste time on easily solvable issues – or miss them altogether.
Finding the right reviewers
If there’s one area where AI has transformed operations most profoundly, it’s reviewer selection.
Matching the right manuscript to the right expert is deceptively complex. A senior editor might have an extensive personal network, but even they can’t know every promising researcher working in shifting or specialised subfields.
That’s where AI proves invaluable, parsing millions of publication records, analysing keywords, subject categories, citation webs, and even co-authorship patterns to identify likely experts.
These tools don’t just spit out lists; they often provide ranked suggestions with reasons, highlighting why a reviewer might be a strong match, perhaps based on shared methods, similar datasets, or recent citations of the same foundational studies. They also cross-check for apparent conflicts, like recent collaborations.
Even so, human editors must still exercise crucial judgment. They’re the ones who weigh diversity, workload balance (has this reviewer already handled three manuscripts this month?), and professional reputation. With AI handling the initial sweep, editors can spend more time on these meaningful choices, rather than simply hunting down email addresses.
Supporting decisions, not making them
Some AI platforms go further, offering editors predictive dashboards. These might assign a ‘fit score’ indicating how closely a manuscript aligns with the journal’s past content or scope. Others flag text that appears to be machine-generated, helping to spot papers churned out by unscrupulous operators trying to game the system.
Specific tools also perform statistical consistency checks, reviewing reported p-values and sample sizes to catch arithmetic slip-ups that could undermine conclusions. They might highlight when a result seems too neat or when a dataset’s variance doesn’t match reported summary statistics.
Crucially, though, these systems provide signals, not decisions. Humans still must interpret whether a flagged inconsistency is a fatal flaw or a fixable oversight. They bring context, for example, is this an emerging technique with few comparators? Is the study design innovative enough to merit careful guidance rather than rejection?
Keeping reviewers accountable
It’s not just manuscripts under scrutiny. Some editorial platforms now use AI to monitor the quality of peer review. Algorithms can detect overly short or generic reviews, for example, boilerplate praise with no substantive critique. They can flag reports that lack any mention of figures, data, or methodology, which might suggest a cursory read.
This is delicate territory. Good reviews vary widely in style and length across different fields. A thoughtful two-paragraph summary in one domain might be more insightful than a rambling three-page treatise elsewhere. So again, humans interpret these metrics. Editors use AI-generated alerts to prompt closer reading, rather than disqualifying reviews outright.
By encouraging more complete and relevant feedback, these tools indirectly improve the author experience, helping ensure that peer review fulfils its purpose as constructive, not just gatekeeping.
A day in the life: AI in an editor’s workflow
So, what does this actually look like on a day-to-day basis? Picture it: it’s Monday morning. An editor logs in to find fifteen new submissions. Rather than wading through them line by line, she sees an AI-generated overview.
- Three papers flagged for language editing first – too many fragmented sentences for smooth assessment.
- Two are tagged with possible text overlap from existing literature, needing a closer manual look.
- One is missing a clinical trial registration number, prompting an immediate author query.
Of the rest, the system has suggested potential reviewers, complete with overlap maps showing shared methodologies. It has even highlighted one reviewer as being recently overburdened, suggesting an alternative. The editor quickly filters the suggestions, adds a human pick of her own, and sends out invitations.
In the afternoon, she checks the peer reviews returned last week. A quality scan suggests that two are unusually short. She reads them – one turns out fine, a focused commentary from a known expert; the other is vague. The editor invites a third reviewer to ensure robust feedback.
Throughout, the AI acts as a silent assistant, never making final calls, but smoothing hundreds of small steps that would otherwise clog the day.
Why humans, not machines, still anchor peer review
For all this impressive automation, peer review remains irreducibly human. Why?
- Judgement is fundamentally interpretive. Deciding whether a paper advances knowledge or merely restates known findings isn’t binary. It requires individuals who understand the context, competing theories, and evolving disciplinary standards.
- Ethics can’t be mechanised. AI might flag missing statements, but it’s humans who weigh conflicts of interest, who decide whether a late-career author deserves leniency over a missed protocol registration, or whether a borderline case warrants mentorship instead of rejection.
- AI lags behind the cutting edge. Language models learn from published data – the past. They may miss truly novel intersections, interdisciplinary fusions, or early-stage methods that haven’t yet generated a citation trail.
- Academic publishing is a cultural system. What’s persuasive or compelling often hinges on subtle norms, implicit debates, or collective aspirations of a field. Machines can’t adjudicate these values; humans have to.
The ethics question: trust, bias, transparency
As AI gains more influence, so do the stakes. Researchers increasingly ask:
- Who trains these models?
- Do they inadvertently replicate existing biases: favouring well-funded topics, elite institutions, or English-language norms?
- Are authors told when their manuscripts are run through predictive acceptance tools?
Some publishers now explicitly list their AI screening practices in submission guidelines. Others are working towards transparent audit trails that let editors see exactly why a flag was raised. And this kind of scrutiny is only likely to grow, rightly so, since trust in peer review depends on fair and explainable processes.
Done right, it’s a partnership
At its best, AI in peer review is like a meticulous research assistant. It never tires of cross-checking references, never overlooks a small compliance error, and never forgets who reviewed last month. But it also never pretends to weigh the intellectual value of a bold hypothesis or a daring new dataset.
That’s what people are for.
When machines handle repetitive tasks, humans gain space to engage deeply: to mentor new authors, explore complex ideas, and shape the scientific record not just efficiently, but wisely.
Final thoughts…
So, what’s next?
Expect more. More integrations that seamlessly track manuscripts from submission through revision to final publication. More sophisticated linguistic models that might soon detect argument gaps or ambiguous claims. More robust data on reviewer performance that helps editors balance workloads fairly.
But also expect more guardrails – clearer standards, stronger disclosure, rigorous oversight. Because the goal isn’t faster peer review at any cost. It’s peer review that’s both rigorous and humane, protecting the time and care scholars pour into advancing knowledge.
About PA EDitorial
At PA EDitorial, we see this as more than a technical upgrade. It’s a chance to build stronger editorial systems – where AI handles the scanning, and experienced humans handle the thinking.
We bring these two sides together by sourcing and managing trusted integrity tools, interpreting what the alerts mean in practice, and guiding editors on clear next steps. The result? A peer review process that’s faster and more robust, yet always grounded in human judgement.
If you’re exploring how to integrate AI checks into your editorial workflows (or just want to be ready for what’s next), we’d be glad to help.
Get in touch – let’s ensure technology strengthens your editorial decisions and never overrides them.