By automating initial evaluations, AI tools can quickly identify relevant literature, check for plagiarism, and assess the quality of submitted articles, allowing reviewers to focus on substantive observations. However, challenges include ensuring transparency and accountability, as AI systems can introduce biases if not managed properly. To maintain ethical standards, clear guidelines and oversight are essential, along with human reviewers capable of interpreting AI-generated insights. Currently, AI applications in peer review include manuscript screening and data analysis, streamlining the workflow while maintaining the rigor of the review process.

AI has significant potential to enhance the peer review process, but it must be designed and applied responsibly to uphold research integrity and ethical standards.

1. Efficiency & Speed

• AI tools can assist editors by filtering low-quality or off-topic submissions before sending to reviewers.
• NLP models can summarize long manuscripts, extract key findings, and highlight unusual claims, reducing reviewer fatigue.
• Automated detection of plagiarism, reference quality, and formatting issues improves throughput.

2. Accuracy & Consistency

• AI-based checklists can ensure consistent application of evaluation criteria across reviewers.
• Tools like SciScore assess methodology transparency and rigor in life sciences.
• AI can flag statistical errors, missing data, or inappropriate experimental design.

3. Fairness & Bias Mitigation

• AI can assist in blinded reviewer matching to reduce conflicts of interest.
• Algorithmic suggestions can ensure diverse reviewer pools across geography, gender, and career

As a peer reviewer, I find AI tools practically useful for initial screening—spotting statistical anomalies or plagiarism that might miss. But—and this is critical—I’ve observed their limitations in contextual understanding.

I would also agree that AI can rapidly detect plagiarism and anomalies in data. Another potential use, but one that carries dangers also, is in checking references. Do the references actually exist and are they being accurately cited, but against this is that AI has been known to invent references to support a desired conclusion and care would be needed to ensure that using AI to check sources did not make such invented sources easier to conceal.