Abstract
Traditional Chinese Medicine (TCM) is a system of medical concepts, diagnostic methods, and therapies with a rich and ancient history in China. While TCM has long been a cornerstone of healthcare in Chinese communities, its influence and popularity are much less realized on a global scale. The public’s understanding of TCM can be largely affected by the most common sources of accessible information online, especially with the emergence and quick development of AI modern technologies in recent years, as these tools are potentially becoming a primary source of knowledge for many, thus posing a significant impact on how people perceive and learn about TCM. This raises an important question about the accuracy of AI-generated responses and the potential consequences of misrepresenting TCM in public. This study evaluated the accuracy of AI responses on TCM research by comparing them to bibliometric analysis, which is traditionally used to analyze academic publications and trends. The findings show that AI is effective at identifying broad, general patterns in TCM research. However, it often lacks the nuanced details and specific insights that a thorough bibliometric analysis can provide. Despite these limitations, the study still revealed a promising synergy between the two approaches. AI can assist bibliometric analysis by helping quickly capture general research categories that would be time-consuming to identify manually. This suggests that integrating the power of AI with traditional bibliometric methods could lead to unexpected benefits, offering a more efficient and comprehensive way to understand the complex landscape of TCM research.
Keywords: AI Chatbots, Bibliometric Analysis, Research Theme, Traditional Chinese Medicine.