The use of AI in peer review has been gaining attention, with some proponents suggesting it could significantly enhance the efficiency of the scientific publishing process. However, Md Doulotuzzaman Xames, a researcher from Virginia Tech, raises concerns that the implementation of AI in this crucial step could compromise the credibility of the entire peer-review system. In his letter published in Nature on April 29, 2025, Xames draws from his personal experiences as both a reviewer and an author to highlight potential pitfalls. He argues that the current peer review system, despite its flaws, maintains a level of human judgment and critical thinking that is essential for evaluating the nuances and complexities of scientific research. AI, while capable of automating routine tasks and identifying grammatical and formatting errors, lacks the ability to contextualize research within broader scientific discourse or to discern subtle issues in methodology and experimental design. One of the primary concerns Xames raises is the potential for "black box" decision-making, where the criteria and processes used by AI systems are opaque and difficult to scrutinize. This could lead to a lack of transparency and accountability, making it challenging for the scientific community to challenge or understand the decisions made by these systems. Additionally, he warns that AI systems might be biased if they are trained on existing datasets that reflect historical biases, potentially perpetuating these biases rather than addressing them. Xames also points out that the use of AI in peer review could erode trust among scientists. The process of peer review is built on the principle of expert evaluation by fellow scientists, and replacing this with automated systems might make researchers feel disconnected from the vetting process. This could further complicate efforts to address issues such as research misconduct and the replication crisis, which already plague the scientific community. Another significant concern is the potential for AI to overlook the unique contributions and insights of manuscripts, particularly those that propose new or unconventional theories. Xames suggests that AI systems, due to their reliance on predefined rules and algorithms, might struggle to appreciate the innovative aspects of such research. This could stifle creativity and limit the diversity of ideas that are essential for scientific progress. Despite these concerns, Xames acknowledges that there are areas where AI could be beneficial. For instance, AI could assist in the initial screening of manuscripts by identifying technical issues or flagging potential plagiarism. However, he emphasizes that such systems should be used as tools to support human reviewers, not replace them entirely. He advocates for a hybrid approach that leverages the strengths of AI while retaining the critical human element in the peer review process. The broader implications of AI in peer review extend beyond individual research papers. If the credibility of the peer review process is undermined, it could have far-reaching consequences for the scientific community, including potential skepticism from the public and policymakers. Xames urges the scientific community to proceed with caution and to thoroughly evaluate the ethical and practical implications of integrating AI into the peer review system. Industry insiders have also weighed in on the debate. Dr. Jane Robertson, a peer review specialist at a leading scientific journal, agrees with Xames's concerns but adds that AI could play a valuable role in reducing the administrative burden on human reviewers. She suggests that journals should invest in training AI systems to work alongside human experts, ensuring that the systems are transparent and can be audited for fairness and accuracy. Robertson also emphasizes the importance of creating clear guidelines and standards for AI use in peer review to prevent misuse and maintain the integrity of the scientific literature. Virginia Tech, where Xames is based, is known for its strong research programs in various fields, including engineering and computer science. The university's commitment to interdisciplinary research highlights the need for careful consideration of how AI can be integrated into complex academic processes like peer review. In conclusion, while AI has the potential to streamline and improve certain aspects of the peer review process, it must be approached with a nuanced understanding of its limitations and potential risks. A balanced and hybrid model that complements human expertise with AI capabilities seems to be the most viable path forward, ensuring that the scientific community continues to benefit from rigorous and credible peer reviews.