For generations, peer review has been a cornerstone of scholarly publishing. Before research enters the academic record, experts evaluate its methodology, evidence, originality and conclusions. The system is imperfect, but its purpose remains essential: protecting the credibility and integrity of scholarship. The Scholarly Kitchen brought this interesting topic to our attention in their article,”Guest Post — Do we need (r)evolution in peer review?“

Artificial intelligence (AI), however, is challenging some of the assumptions on which traditional peer review was built.

Researchers can now use AI to analyze large datasets, conduct literature reviews, identify patterns, generate code and assist with drafting. At the same time, generative AI can produce convincing text, citations, images and even data that may be inaccurate, fabricated or difficult to distinguish from authentic and legitimate research. Reviewers are being asked to evaluate not only what researchers concluded, but how AI contributed to the work.

That may require peer review to evolve. Review standards could include clearer disclosure of AI tools, models and prompts when they materially influence research. Reviewers may need greater access to underlying datasets, code and methodological documentation to verify AI-assisted findings. Publishers may also need reviewers with expertise in AI, data governance and algorithmic bias alongside traditional subject-matter experts.

AI itself could support the review process by flagging statistical anomalies, checking references, identifying potential methodological weaknesses or helping reviewers navigate increasingly complex research. But using AI to police AI creates its own concerns around transparency, bias and accountability.

The answer is not replacing human peer review with machines. It is strengthening human judgment for a research environment in which machines are increasingly involved. Peer review has always evolved alongside scholarship. AI simply makes the next evolution more urgent.

AI only works as well as the structure behind it. Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.