A Stanford University study found that up to 17% of peer reviews in major computer-science publications are now AI-generated. The research analysed 50,000 peer reviews from recent years, identifying AI-generated reviews by their formal tone, verbosity, and superficial content. The use of AI for peer reviews is attributed to time constraints. The study raises concerns about scientific integrity and AI limitations in providing in-depth reasoning. Recommendations include establishing guidelines for responsible AI use in peer reviews, fostering more human interactions, and developing innovative platforms adapted to the AI age. Further research on responsible AI assistance in peer reviews is emphasised to maintain scientific integrity.

**For academic publishers and research institutions:** You're facing a critical challenge to the integrity of your peer review process. This AI infiltration demands immediate action. Implement robust detection systems and clear guidelines on AI use in reviews. Consider introducing mandatory declarations from reviewers about AI assistance. **For AI ethics committees and regulatory bodies:** You need to fast-track the development of standards for AI use in scientific processes. This trend could spread to other industries, affecting quality control and expert evaluation processes. **For AI developers:** There's a pressing need for transparent AI tools that can assist in peer review while maintaining accountability. Focus on developing AI that can flag potential conflicts, inconsistencies, or areas requiring human expertise.

Expect a rapid evolution in peer review practices. We'll likely see the emergence of AI-human hybrid review models, where AI assists in initial screening and humans provide in-depth analysis. Publishers may introduce AI-powered platforms that facilitate more efficient, interactive reviews while ensuring human oversight. Watch for the development of specialized AI tools designed for scientific review, capable of deep domain understanding. The academic community will need to adapt, potentially requiring new skills in AI literacy and ethical AI use. This trend may extend beyond academia, influencing how businesses conduct internal reviews, audits, and quality assessments. Prepare for a shift in how we perceive expertise and authority in various fields as AI becomes more prevalent in evaluation processes.