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February 2, 20260 citationsOpen Access

Artificial Intelligence in the Editorial and Peer Review Process: A Protocol for a Cross-Sectional Survey of Traditional, Complementary, and Integrative Medicine Journal Editors’ Perceptions

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JNJeremy Y. NgDBDaivat BhavsarNDNeha Dhanvanthry

Key Points

  • This research aims to understand TCIM journal editors' perceptions of artificial intelligence in the publishing process.
  • Conduct a cross-sectional survey targeting editors of TCIM journals.
  • Use purposive sampling to collect contact details from journal websites.
  • Administer the survey online via SurveyMonkey, with follow-up invitations.
  • Include sections on demographics, AI familiarity, benefits, challenges, and ethical concerns.
  • Anticipate insights regarding acceptance and barriers to AI in TCIM publishing.
  • Expect varied perceptions that could inform future AI tool development.

Abstract

This research protocol outlines a cross-sectional survey study, aimed at editors from traditional, complementary, and integrative medicine (TCIM) journals, regarding their perceptions of the use of artificial intelligence (AI) in the editorial and peer review process. The survey will be sent to editors-in-chief, associate editors, and editorial board members of TCIM journals (100-150). The research involves purposive sampling via manual collection of contact details from TCIM journal websites. The survey will include sections on demographics, current use and familiarity with AI, perceived benefits and challenges, ethical concerns, and the outlook for AI in publishing. Data collection will be conducted online using SurveyMonkey whereby email invitations and follow-up reminders will be sent to potential respondents. Quantitative data will be analyzed using descriptive statistics, and for qualitative data, thematic analyses will be employed. This protocol study aimed to devise a survey which could provide insight into the acceptance and potential barriers to AI adoption in TCIM publishing from an editor’s perspective. The results of which may later guide the development of AI tools in a way that aligns with the needs and values of the TCIM research community.

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Cite This Study

Ng et al. (2025) studied this question.

synapsesocial.com/papers/6980fd60c1c9540dea80f17dhttps://doi.org/10.5167/uzh-283712
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