AI in Peer Review: the PA EDitorial Advantage
AI gives us speed. Human editors give you trust.
When it comes to safeguarding research integrity, AI tools are no longer an abstract promise. They’re here and fast becoming part of everyday editorial workflows.
At PA EDitorial, we’ve seen this shift first-hand. It’s not surprising that with tools like Papermill Alarm, ImageTwin, Signals Manuscript Check, Paperpal Preflight, and Alchemist Review now integrated into platforms like ScholarOne and Editorial Manager, many publishers are asking the same question: If AI can flag the problems, what’s left for us humans to do?
But… most AI tools stop at the alert; we don’t.
At PA EDitorial, we translate automated flags into trusted editorial decisions, combining human judgement, policy insight, and cross-platform experience to guide teams through complex integrity issues with clarity and confidence.
We go beyond the ‘red’ score by interpreting AI outputs in the context of the journal’s scope, editorial history, and publication standards, helping to distinguish real risks from false positives.
The rise of AI tools has undoubtedly expanded the range of checks and insights available at the peer review stage. But with this comes a new challenge: more information doesn’t equal more capacity. Editors and publishers are faced with an increasing volume of outputs to interpret, without the time to do so.
We see a role at PA EDitorial for us to absorb that burden, supporting the human judgement that still sits at the heart of editorial decision-making. AI tools surface more, not less, for journal teams to deal with. We help make sense of it all, so precious editorial time isn’t stretched even thinner.
The integration wave is here
We’re proud to work alongside publishers and partners who are already integrating AI into their submission platforms, such as ScholarOne and Editorial Manager, where integrated tools help flag potential integrity concerns early in the process.
Some of the key players in this space include:
- Papermill Alarm: Flags potential paper-mill activity, including high-volume submissions from the same authors, inconsistent metadata, templated language, and unusually similar content across papers.
- ImageTwin: Detects duplicated or manipulated images within and across manuscripts by scanning for visual similarities and forensic inconsistencies.
- Signals Manuscript Check: Highlights structural and formatting anomalies, such as missing sections, irregular metadata, keyword mismatches, and signs of auto-generated content.
- Alchemist Review: Extracts and assesses a manuscript’s core contributions, evaluating research quality, citation integrity, and originality.
- Paperpal Preflight: Provides a pre-submission check of language, authorship, ethics, references, and formatting, offering actionable suggestions to meet journal requirements and improve clarity.
All of these tools are designed to catch issues that might otherwise slip through the net, but alerts alone don’t tell the whole story. That’s why our role as human interpreters is so important.
For example, a flagged image might turn out to be a reused template. An unusual citation pattern might reflect a genuine niche topic. A flagged manuscript might contain no breach at all. This is where the human layer matters.
Beyond the alert
Our human experts are trained to understand the editorial implications of AI outputs. We assess the risk, context, and intent behind each flag, and we support editors in making clear, defensible decisions.
That might mean:
- Explaining when a flagged image is a false positive
- Clarifying whether a citation cluster reflects genuine review or manipulation
- Identifying patterns consistent with known paper-mill strategies
- Supporting new editors in interpreting their first AI report
- Helping teams build internal policies around AI findings
Our aim is simply to provide the thinking layer that AI doesn’t have and help editors navigate these tools without losing their grip on fairness, consistency, or trust.
The human-in-the-loop model
At PA EDitorial, we believe the most effective integrity systems don’t replace human judgment; they enhance it. Automation does the heavy lifting, but people provide the perspective.
Our approach is to:
- Let AI do the scanning
- Ensure humans do the interpreting
- Give editors the support they need to act confidently
This human-in-the-loop model isn’t just efficient; it’s also ethically necessary. Researcher careers, reputations, and lives depend on the decisions we make. And while AI can assist, it shouldn’t be the final arbiter.
That’s why our support isn’t just technical; it’s editorial, contextual, and always human.
What’s next?
We’re rolling out a campaign to support editors and publishers at every stage of their AI journey.
Over the coming months, we’ll be sharing resources, blog posts, and insight-driven polls to better understand how the community is feeling about AI and to offer practical guidance on using these tools with human oversight.
We’ll also be spotlighting our integration partners and highlighting real examples of editorial teams combining automation with expert judgement to strengthen trust and efficiency.
We’ll be introducing tools to help teams assess their current stage on the AI adoption curve and what the next steps might look like.
So, if you’re integrating tools like Papermill Alarm, ImageTwin, Signals Manuscript Check, Paperpal Preflight or Alchemist Review, or if you’re just exploring how AI might work in your journal systems, I’d love to talk. This is a pivotal moment for publishing, and we’re ready to help you navigate it.
As editorial processes become increasingly automated and pressures on peer review demand greater efficiency, speed is essential – but trust is still built and maintained through human judgment.
Let AI do the scanning. Let us do the thinking.