How to Use AI for Peer Review: What It Can and Cannot Do

The phrase "AI peer review" means different things depending on who uses it. For journal editors, it may mean using AI to screen submissions before sending them to human reviewers. For authors, it often means using AI tools to check a manuscript before submission. These are separate workflows with different risks and different standards. This guide focuses on the practical question: where does AI add real value in manuscript evaluation, and where does it fall short?

What Does AI Peer Review Mean?

Traditional peer review is the process by which independent experts evaluate a manuscript's scientific merit, methodological soundness, and contribution to the field before it is published. AI cannot replicate this. What AI can do is handle the technical, rule-based checks that are a necessary but separate part of manuscript evaluation: formatting compliance, language quality, reference verification, and structural integrity.

Confusing these two tasks creates unrealistic expectations. AI does not evaluate the novelty of your hypothesis or the validity of your statistical approach. It does check whether your reference list follows Vancouver style, whether your abstract is within the word limit, and whether your figures are labelled correctly. Both types of review matter. They happen at different stages and require different tools.

What AI Handles Reliably

AI tools perform consistently well on tasks that are rule-based, volume-sensitive, and prone to human fatigue. The following three areas are where AI provides the most measurable benefit in manuscript preparation.

Formatting and structure checks

Most journals have precise formatting requirements: word count limits, margin sizes, section order, figure file formats, and heading styles. Checking all of these manually after multiple revision rounds is error-prone. An AI tool can verify compliance against a target journal's Author Guidelines in minutes, flagging deviations before the manuscript reaches a human editor.

• Abstract word count within the journal's specified limit
• IMRaD section order (Introduction, Methods, Results, Discussion)
• Figure and table numbering and caption placement
• Ethics declaration and conflict-of-interest statement present
• Manuscript file format and margin compliance

Language and clarity checks

Grammar errors, inconsistent terminology, and unclear sentence structure are among the most common reasons for revision requests in journals that accept non-native English submissions. AI language checkers trained on academic text can identify these issues with higher consistency than a single human reader, and without the fatigue that affects manual review of long documents.

What makes AI language review useful is specificity: rather than a general impression, it produces a score, flags the exact sentences that need attention, and in many tools produces a tracked-changes document in MS Word. This gives authors a concrete list to work from, not a vague instruction to "improve the English".

Reference accuracy and style

A manuscript with 50 references contains hundreds of individual data points: author names, publication years, journal names, volume numbers, page ranges, and DOIs. Manual verification of all of these against a specific citation style (APA, Vancouver, Harvard) takes hours and is highly error-prone. AI reference checkers can scan the entire reference list, identify the citation style, and flag every inconsistency in minutes.

What AI Cannot Replace

The areas where AI fails in peer review are the same areas that make peer review valuable in the first place. No current AI system can reliably evaluate:

• Whether the research question is novel or already answered in the literature
• Whether the methodology is appropriate for the research design
• Whether the conclusions are supported by the data presented
• Whether the statistical analysis is correctly applied and interpreted
• Whether the study adds meaningful contribution to the field

These judgments require domain expertise, familiarity with the current state of the field, and the ability to reason about scientific validity. Researchers and journal editors who use AI to replace this type of evaluation create serious risks for publication quality and research integrity.

AI also has a known tendency to hallucinate: to produce plausible-sounding but incorrect information. For this reason, AI-generated summaries of scientific content should never be treated as authoritative without verification by a qualified human reader.

How to Combine AI and Human Review

The most effective approach treats AI as a preparation tool and human reviewers as the evaluators of scientific merit. This division of labor makes both more effective. Human reviewers are freed from spending their time on formatting corrections and grammar issues. Authors arrive at peer review with a cleaner, more compliant manuscript, which reduces revision cycles and accelerates publication timelines.

For journal editors, using AI to screen submissions before routing to peer reviewers reduces the administrative burden of non-compliant manuscripts. Submissions that fail basic formatting or language thresholds can be returned to authors for correction before review begins, protecting reviewer time and improving the overall quality of the review pool.

A Practical Workflow

Here is a practical sequence for authors who want to use AI effectively in the pre-submission stage:

1. 1Complete your manuscript draft, including all sections, references, figures, and declarations.
2. 2Run an AI formatting check against your target journal's Author Guidelines. Fix flagged deviations.
3. 3Run an AI language check. Review the flagged sentences and apply corrections where the suggestions are accurate.
4. 4Run an AI reference check. Verify the style, completeness, and DOI accuracy of flagged references.
5. 5Read the full manuscript yourself or ask a colleague to read it for scientific coherence and logical flow. AI does not substitute this step.
6. 6Submit.

This sequence ensures that when a human reviewer reads your manuscript, they are evaluating the science, not correcting the formatting. It also reduces the probability of desk rejection, which delays publication without any scientific feedback.

Using PoolText for Pre-Submission Checks

PoolText runs the technical checks described above: formatting compliance, language quality, reference accuracy, reference actuality, and citation style. You upload your manuscript, select your target journal or citation style, and receive a structured report in minutes.

The Language Report covers grammar, clarity, academic register, and produces an MS Word file with tracked corrections. The Full Report adds structural checks (20+ rules), reference accuracy and actuality, table and figure validation, and style compliance. Neither report evaluates scientific merit. That is intentional: PoolText handles the technical layer so that human reviewers can focus on the scientific one.