This study analyzes the current status of research on integrative medicine combining traditional East Asian medicine (TEAM) and Western medicine (WM) using artificial intelligence and data science approaches, with a focus on diagnostic applications. Through a comprehensive literature review, we identified and categorized relevant studies into three types: 1) models utilizing both TEAM and WM information for various diagnoses, 2) studies performing Western medical diagnoses based on TEAM information, and 3) research analyzing the correlation between TEAM diagnoses and Western medical information. For each category, we summarized the research objectives, sample sizes, collected TEAM and WM factors, integration methods, and key results in terms of the benefits of integrative medicine. Our analysis revealed that integrating TEAM and WM information often led to improved diagnostic accuracy and more comprehensive patient profiles compared to single-system approaches. However, we also identified several limitations in current research, including small sample sizes, lack of external validation, and difficulties in standardizing TEAM data. Based on our findings, we propose future research directions to address these limitations, such as conducting larger-scale prospective cohort studies, developing standardized methods for collecting and digitizing TEAM data, and investigating the underlying physiological mechanisms of observed correlations between TEAM and Western medical factors. This review provides valuable insights into the current state and future potential of data-driven integrative medicine research, highlighting its promise in enhancing diagnostic accuracy and patient-centered care.
Sa-Yoon Park (Sun,) studied this question.