For Immediate Release
Peer Review Week (PRW) is a community-organized, five-day celebration recognizing the significance of peer review in scientific research. Since its launch in 2015, reviewers, researchers and institutions have utilized PRW to share resources and advancements related to peer review. Each year is themed to highlight a timely and relevant topic, such as Diversity in Peer Review (2018) and Peer Review and the Future of Publishing (2023), among others.
This year’s theme, Rethinking Peer Review in the AI Era, is representative of the rapidly developing technology that’s redefining productivity, research and aspects of day-to-day life. To understand the applications of AI in peer review, we look to SLAS Technology Associate Editor, Chang Liu, PhD (SCIEX), and SLAS Discovery Editor, Elizabeth Sharlow, PhD (University of Virginia), for their professional insights as we examine key elements of this theme.
Liu: AI serves as a supporting tool rather than a decision-maker. The reviewer could use the AI tool for detecting plagiarism, accelerating the literature search, etc., but the reviewer determines the scientific merit and novelty of the work through critical thinking. The reviewer could state what AI tools, if any, were used in the process.
Sharlow: AI can enhance the peer review process by (1) pinpointing potential bias in peer feedback, (2) checking for plagiarism, and/or (3) confirming references. On another level, AI potentially can help editors match manuscripts with reviewers whose expertise and publication history align with the manuscript’s message. Used responsibly, AI can refine the review process as well as elevate fairness, accuracy and relevancy.
Liu: The reviewer should disclose if any AI tools were utilized during their review. Also, confidential information present in the manuscript should be protected and not disclosed to the public [via AI tools] without consent.
Sharlow: Journals should be transparent about when and where they use AI tools in the manuscript review process. Overall, AI should help, but not replace, human judgment, with final decisions remaining with editors and reviewers.
Liu: Workshops and tutorials will be helpful, with some example case studies. Training sessions for reviewers providing guidance/case study examples, detailing how to use the AI tools in the peer review process responsibly.
Sharlow: We need to remind peer reviewers of ethical principles such as fairness, transparency and accountability. Providing learning resources that focus on AI challenges, including limitations and hands-on demonstrations of capabilities, as well as integrating AI into workstreams, would also be valuable.
Liu: The critical review to assess the novelty and the impact of the study must remain uniquely human. Additionally, the AI tool may not be up-to-date (i.e., state-of-the-art) for the most cutting-edge technologies. This discrepancy is because AI tools might not have the latest information available during their training. Therefore, it may not be the most effective tool for judging the novelty of a work. It will still rely on the reviewer’s expertise.
Sharlow: Human reviewers have the potential to deliver crucial feedback in a tactful and effective manner. This is especially critical for manuscripts from early-stage investigators, where peer reviewer feedback can guide authors toward manuscript improvement or refinement rather than just listing flaws and mistakes.
Liu: AI is an assistant, but not a replacement for the peer-review process. It can help verify references and refine grammar, but the assessment of the impact and novelty of the work requires the expertise and critical judgment of the reviewer.
Sharlow: Don’t use AI tools because they are en vogue. Take the time to understand how AI tools work and identify their weaknesses. A peer reviewer who understands the limits of an AI tool is more effective than one who simply follows its suggestions.
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SLAS (Society for Laboratory Automation and Screening) is an international professional society of academic, industry and government life sciences researchers and the developers and providers of laboratory automation technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.
SLAS Discovery reports how scientists develop and use novel technologies and/or approaches to provide and characterize computational, chemical and biological tools to understand and treat human disease. The journal focuses on drug discovery sciences with a strong record of scientific rigor and impact, reporting on research that:
SLAS Technology reveals how scientists adapt technological advancements for life sciences exploration and experimentation in biomedical research and development. The journal emphasizes scientific and technical advances that enable and improve:
SLAS Discovery: Advancing the Science of Drug Discovery, 2024 Impact Factor 2.7. Editor-in-Chief Robert M. Campbell, Ph.D., Grove Biopharma, Inc., Chicago, IL (USA)
SLAS Technology: Translating Life Sciences Innovation, 2024 Impact Factor 3.7. Editor-in-Chief Edward Kai-Hua Chow, PhD, KYAN Technologies, Los Angeles, CA (USA).
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Jill Hronek
Director of Marketing Communications
Telephone: +1.630.256.7527, ext. 103
E-Mail: jhronek@slas.org
