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Traditional, Complementary, and Integrative Medicine Researcher Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process: A Protocol for a Large-Scale, International Cross-Sectional Survey
Jeremy Y. Ng, Jamie Tan, Karthik Adapa, Prashant Kumar Gupta, Shao Li, Darshan Mehta, Melinda Ring, Manisha Shridhar, João Paulo Souza, Tetsuhiro Yoshino, Myeong Soo Lee, Holger Cramer
Perspect Integr Med. 2026;5(1):59-64.   Published online February 11, 2026
DOI: https://doi.org/10.56986/pim.2026.02.009
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Graphical AbstractGraphical Abstract AbstractAbstract PDF
Background
Generative artificial intelligence (GenAI) chatbots can simulate conversations and perform tasks typically performed by humans, and offer novel research opportunities. Specifically, GenAI chatbots have shown utility in assisting with literature reviews, and interpreting large datasets, among other labor-intensive tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and artificial intelligence (AI) presents numerous key opportunities. However, TCIM researchers’ attitudes and perceptions of the role of GenAI tools in the scientific process remain less understood.
Methods
This protocol for a large-scale, international cross-sectional web-based survey was designed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the research process. Emphasis will be placed on the advantages, limitations, and the ethical implications of GenAI chatbots use. The survey will be sent to TCIM researchers who have previously published in the field (anticipated 3%-7% response rate). It will include questions regarding demographic information, familiarity with AI chatbots, perceived benefits, and challenges of AI chatbots in the scientific process, and it will have several open-ended questions. Data will be analyzed using descriptive statistics.
Conclusion
By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, enabling greater trust and acceptance surrounding the use of GenAI. Furthermore, findings from this survey will be integral to gaining insight into the perceived challenges of TCIM-driven AI, which will be vital in guiding future policies and collaborations among researchers.
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