Can AI help solve the peer-review crisis? Here are its promises and pitfalls (www.science.org)

Recent discussions have emerged around the potential of artificial intelligence to address the ongoing crisis in academic peer review, marked by delays, inconsistencies, and a backlog of submissions. AI technologies, such as natural language processing and machine learning algorithms, can streamline the review process by automating the assessment of research quality and relevance, which could enhance efficiency and accuracy in evaluating manuscripts. This could help alleviate the pressures faced by overwhelmed reviewers and improve the overall flow of academic publishing. However, leveraging AI in peer review also raises significant concerns. The reliance on algorithms might introduce biases, as many models can perpetuate existing disparities found in training data. Additionally, AI lacks the nuanced understanding of context and the subjective evaluation process that human experts provide, which could lead to oversights in critical assessments. Balancing the implementation of AI tools with the essential human touch in peer review will be crucial, as the academic community seeks to enhance the integrity and reliability of research evaluation while also resolving the systemic challenges it currently faces.