Attitudes and Perceptions of Generative Artificial...

Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large‐Scale, International Cross‐Sectional Survey

Abstract

ABSTRACT Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient‐centric approach that emphasizes holistic well‐being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers’ attitudes toward GenAI tools remain less understood. This large‐scale, international cross‐sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods A search strategy in MEDLINE (via Ovid) identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on sociodemographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analyzed using descriptive statistics and thematic content analysis. Results A total of 716 survey responses were received. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (84.08%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Conclusions By deepening understanding of TCIM researchers' perspectives, these findings can inform future GenAI applications, policies, and collaborations in the field.