Research at the intersection of Traditional, Complementary, and Integrative Medicine and Artificial Intelligence: A protocol for a bibliometric analysis

ABSTRACT: Background: Traditional, Complementary, and Integrative Medicine (TCIM) describes a broad collection of medical interventions, practices, and belief systems that fall outside the purview of conventional medicine. Accompanying the recent growth of TCIM research productivity, Artificial Intelligence (AI) technologies have increasingly impacted areas of biomedical research, including diagnosis, treatment planning, and drug discovery. Despite the applicability of AI to TCIM research, the intersection of these topics remains underexplored. Methods: A search string encompassing terms related to TCIM and AI will be run on MEDLINE, with no restrictions by date, language, or publication type. Retrieved MEDLINE records will subsequently be used as the source for Digital Object Identifier citation searches in Scopus. No manual cleaning of MEDLINE or Scopus records will be performed, given the comprehensive search strategy and the complexities of defining the TCIM scope. The following bibliometric data will be collected: number of publications (including total publications and publications per year), open access status, subject area, document type, publication stage, publications per journal, author affiliations, funding sponsors, publication country, source type, and publication language. Performance analysis indicators (e.g., citations and publications) will be identified and presented. Bibliometric network maps will be visualized using VOSviewer, with thematic clusters identified and presented. Conclusions: This bibliometric analysis study will provide insights into research productivity at the intersection of TCIM and AI, informing future research directions and policy development.