{"id":44231,"date":"2023-09-26T20:54:18","date_gmt":"2023-09-26T14:54:18","guid":{"rendered":"https:\/\/www.enago.com\/academy\/?p=44231"},"modified":"2023-09-29T15:19:28","modified_gmt":"2023-09-29T09:19:28","slug":"ai-integration-in-peer-review","status":"publish","type":"post","link":"https:\/\/www.enago.com\/academy\/ai-integration-in-peer-review\/","title":{"rendered":"AI Integration Can Future-Proof Peer Review \u2014 But only if we define and drive the right principles"},"content":{"rendered":"

In this ever-evolving landscape of scientific research, trust in the integrity, validity, and impartiality of the process of disseminating scientific findings is essential for the progress of humanity. Scholars, researchers, institutions, and consumers of research have long relied on the peer review<\/a><\/strong> step as the gold standard for ensuring the quality and credibility of research publications. The five core elements of peer review are identified. Constitutive elements of scholarly peer review include: fairness in critical analysis of manuscripts; the selection of appropriate reviewers with relevant expertise; identifiable, publicly accountable reviewers; timely reviews, and helpful critical commentary.<\/p>\n

However, as is known, the traditional peer review process is not without its challenges. Among several issues, it has faced criticism for its potential biases, lack of transparency, and the time it takes to publish research. The fact that it is completely a human process also makes it difficult to standardize and super-efficient.<\/p>\n

Through this article, I want to specifically address this question \u2013 can AI help address these issues without compromising the gold standard?<\/p>\n

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