Table of Contents
Peer Review in the Era of AI: Why We Need Hybrid, Not Fully Automated Evaluation
“AI in peer review” has gone from buzzword to business model in just a few years. Tools now promise faster screening, cleaner language, and even “automated reviewing.” At the same time, many researchers worry that the use of AI in peer review could undermine science if we over-delegate judgment to opaque systems.
So which is it—miracle or minefield?
This article argues for a clear middle path: a hybrid peer review model where AI acts as a powerful filter and assistant, but human experts remain the co-reviewers and final decision-makers.
Why Peer Review Exists (and What’s at Stake)
Before we ask what AI can do, we need to remember why peer review exists at all. At its best, peer review is a quality gate that checks:
• Novelty – Is there a genuine contribution beyond what we already know?
• Methodological soundness – Are design, data, and analysis robust and appropriate?
• Ethics and integrity – Were participants respected, data handled responsibly, and approvals in place?
• Proportional claims – Do the conclusions match the evidence, or are they overstated?
AI can help us move manuscripts through the system faster, but if it weakens any of these gates, the cost is huge: retractions, mistrust, and wasted funding. That’s why a thoughtful use of AI in peer review must always support, not replace, expert judgment.
What AI Does Well in Peer Review Workflows
AI is genuinely transformative for the front end of the process. Used well, it saves time and improves consistency.
Language, Formatting, and Compliance Checks
AI models are strong at:
• Improving grammar, style, and clarity.
• Flagging missing sections (methods, ethics statements, references).
• Checking basic structure against journal author guidelines.
Here, AI works like an ultra-fast assistant, not a judge. It helps authors submit more polished manuscripts and helps editors triage obvious out-of-scope or low-quality submissions.
Similarity, Plagiarism, and Basic Data Checks
AI-driven systems also power:
• Similarity and plagiarism detection
• Reference checks for impossible citations or outdated sources
• Simple consistency checks in tables, units, and basic calculations
These tools act as filters that quickly surface anomalies, allowing humans to focus on interpretation and consequences rather than manual hunting.
What Only Human Reviewers Can (and Should) Do
The danger arises when we forget where AI stops. Claims like “using AI in peer review is a breach of confidentiality” come from real concerns: data leakage, unlogged uploads to third-party tools, or models trained on sensitive content. But beyond confidentiality, there are core tasks that remain fundamentally human:
Judging Novelty and Contribution
No AI can reliably answer: Is this genuinely new and important in this field? That requires:
• Awareness of tacit debates and “unwritten” knowledge
• Nuanced reading of citations and positioning
• Value judgments about significance and originality
Novelty is still the domain of experienced researchers and reviewers.
Method–Claim Alignment and Statistics
AI can support statistical checks, but it cannot responsibly decide:
• Whether the design really answers the research question
• Whether the sample is appropriate and unbiased
• Whether the analysis justifies the strength of the claims
That is expert work. AI may highlight issues; humans still need to interpret them.
Ethics, Context, and Confidentiality
Ethical nuance—especially in clinical, social, or community-based research—depends on context, culture, and professional codes. When identity theft in AI conference peer review or similar incidents are reported, it’s a reminder that ethics is not automatable. Humans must:
• Decide what’s acceptable AI use.
• Protect confidential manuscripts and reviewer identities.
• Weigh harm, benefit, and fairness in context.
The Risks of Fully Automated AI Peer Review
If we chase speed at any cost, fully automated AI peer review brings serious risks:
• Overtrust and opacity – Editors or reviewers may defer to a “black box” score without understanding its basis.
• False positives / false negatives – Genuine work may be wrongly flagged as AI-generated or plagiarised, while sophisticated paper-mill outputs slip through.
• Bias amplification – If historical patterns are biased, AI will quietly reproduce them.
• Erosion of trust – Authors and readers lose confidence if decisions seem arbitrary or inexplicable.
In other words, the use of AI in peer review could undermine science if we let machines silently replace human reasoning.
A Practical Hybrid Human–AI Peer Review Model
Instead of “AI vs humans,” we need AI plus humans—with clear division of labour. A practical hybrid workflow can look like this:
Step 1 – Integrity and Compliance Screen (AI-First, Human-Checked)
AI tools perform:
• Language, formatting, and guideline checks
• Similarity and basic plagiarism detection
• Quick scans for missing ethics or consent statements
An editor or internal staff member reviews the flags, decides which are serious, and documents the outcome.
Step 2 – Methods and Data Verification (Human-Led, AI-Assisted)
Subject-matter experts:
• Examine study design, sampling, and data collection
• Use AI to assist with calculations, formula checks, or spotting anomalies
• Decide whether methods and data can support the research claims
Step 3 – Expert Review of Novelty, Claims, and Impact (Human Core)
Peer reviewers:
• Judge originality and contribution to the field
• Evaluate whether claims are proportional to the evidence
• Discuss contextual relevance (local needs, policy, practice)
AI may help summarise references or suggest background, but humans decide.
Step 4 – Final Compliance Pack and Documentation (Hybrid)
Before decision, the editorial team:
• Confirms AI use is disclosed and appropriately limited
• Checks authorship contributions (e.g., CRediT), conflicts, and data/code availability
• Creates a simple “compliance pack” that shows how integrity risks were addressed
This pack is invaluable for audits, appeals, and trust-building with funders and institutions.
A 10-Point Integrity Checklist for the AI Era
Every journal, department, or research office can adapt a short checklist, covering:
1. Clear AI-use disclosure (what tools, for what tasks).
2. Originality and overlap screening completed.
3. Data integrity checks (inconsistencies, duplication, outliers).
4. Image forensics (manipulation, reuse, or fabrication).
5. Appropriateness of statistical methods.
6. Method–claim alignment.
7. Transparent authorship contributions (e.g., CRediT).
8. Ethics approval and participant consent where required.
9. Declared conflicts of interest.
10. Data and/or code availability statements.
AI can assist with some of these, but final responsibility lies with humans.
Strengthen Your Manuscript with Human-Led, AI-Aware Review
If you’re an author or PhD scholar, building your own hybrid workflow from scratch can be overwhelming. One way to de-risk your submission is to use a structured, external pre-submission review.
ManuscriptEdit offers a Peer Review + Journal Selection Report (JSR) starting at 8,000 INR, combining:
• Expert feedback on methodology, originality, and clarity
• Suggestions for refining claims and strengthening integrity statements
• A tailored shortlist of suitable journals
Learn more or get started here:
ManuscriptEdit Peer Review + JSR: https://manuscriptedit.com/PeerReview/
Conclusion: AI as Filter, Humans as Guardians
AI in peer review is here to stay. Used responsibly, it is an excellent filter—speeding up checks, improving language, and surfacing red flags. But it is not yet, and may never be, a full co-reviewer.
The future of trustworthy science lies in hybrid evaluation: AI for scale and consistency, human experts for judgment, ethics, and nuance. When we design our systems around that principle, we protect both the pace and the integrity of research.
Click here to read more interesting blogs.
