Bridging Tradition and Technology: Expert Insights on the Future of Innovation in Peer Review
Innovation and technology are transforming peer review, with artificial intelligence (AI) and automation streamlining tasks, such as plagiarism detection, reviewer selection, formatting, and statistical checking, and significantly boosting efficiency. Yet, concerns around bias, data security, and the potential reduction in human oversight remain central. Additionally, open and virtual peer review practices have been examined for their role in promoting transparency, though they introduce challenges like depersonalization, which can reduce the human element in the review process. Overall, the discussions in this article emphasize the importance of balancing technological advancements with human expertise to uphold fairness and quality in peer review. Introduction The Asian Council of Science Editors (ACSE) hosted an exclusive interview series featuring industry experts who shared insights, ideas, and perspectives on the technology transforming the peer review process (Figure 1). The discussions highlighted critical areas, such as AI-driven automation and open peer review, along with the challenges and opportunities these innovations bring to academic publishing. Open Peer Review: Transparency or Compromise? A strong advocacy for open peer review, in terms of reviewer identity and comment openness, has been maintained, particularly as this mode of peer review has been widely practiced in the field for more than 20 year. However, it is less frequently accepted or utilized (negatively correlated), with the impact factor of the journal in question, as well as with the stage of the researcher’s (peer reviewer’s) career. Although reviewers are generally receptive to the idea of publishing their comments openly and with their names included […]
Authors
- Gareth J. Dyke (ORCID: https://orcid.org/0000-0002-8390-7817)
- Maria J. C. Machado (ORCID: https://orcid.org/0000-0002-2729-4809)
- Jeffrey Robens (ORCID: https://orcid.org/0000-0003-2344-0036)
- Maryam Sayab (ORCID: https://orcid.org/0000-0001-7695-057X)
- Muhammad Sarwar (ORCID: https://orcid.org/0000-0001-9537-2541)
Publication Details
- Journal
- Science Editor
- Published
- 2024-12-04
- DOI
- https://doi.org/10.36591/se-4801-01
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.48