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Get Free AccessBackground: Artificial intelligence (AI) chatbots are advanced conversational programmes capable of performing tasks such as identifying methodological flaws, verifying references, and improving language clarity in manuscripts. Their use in peer review has the potential to enhance efficiency, reduce reviewer workload, and address inconsistencies in review quality. However, concerns remain regarding their reliability, ethical implications, and transparency in decision-making, and little is known about how peer reviewers perceive these tools. Objectives: To assess peer reviewers’ attitudes and perceptions towards the use of AI chatbots in the peer review process, including their familiarity with AI, perceived benefits and challenges, ethical considerations, and expectations for future roles. Methods: An international cross-sectional survey will be conducted among academic peer reviewers. The survey will collect data on participants’ prior experience with AI, perceptions of the utility of chatbots in supporting peer review, concerns related to ethics and transparency, and anticipated future applications. Results: This study will report descriptive and comparative analyses of reviewers’ responses, highlighting patterns in attitudes and perceptions by demographic and professional characteristics. Conclusions: The findings may offer evidence to inform the development of future policies and best practices for the ethical and effective integration of AI chatbots in peer review, with the goal of improving review quality while addressing potential risks.
Jeremy Y. Ng, Daivat Bhavsar, Neha Dhanvanthry, L.M. Bouter, Teresa M. Chan, Annette Flanagin, Alfonso Iorio, Cynthia Lokker, Hervé Maisonneuve, Ana Marušić, David Moher, Holger Cramer (2025). Attitudes and perceptions towards the use of artificial intelligence chatbots in medical journal peer review: A protocol for a large-scale, international cross-sectional survey. , 51, DOI: https://doi.org/10.3897/ese.2025.e159921.
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Type
Article
Year
2025
Authors
12
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.3897/ese.2025.e159921
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