Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration

Abstract

Traditional Chinese Medicine (TCM) is a valuable medical treasure trove that not only demonstrated unique advantages in treating complex and refractory diseases but also left behind a rich legacy of ancient texts and valuable evidence-based medical data based on its human experience for future generations. Nevertheless, the extensive data within TCM has been plagued by challenges, including inadequate data standardization, inconsistent data quality, limited data structuring, and obstacles in interdisciplinary integration. Recent advancements in artificial intelligence (AI) techniques have markedly improved the efficiency and effectiveness with which multimodal data in TCM, including machine learning (ML), deep learning (DL), knowledge graphs (KG), and natural language processing (NLP), particularly large language models (LLMs). These advancements have facilitated more precise data analysis, enhanced clinical decision-making, and improved research outcomes in TCM, such as target discovery, virtual screening of natural products (NPs), symptom differentiation and auxiliary prescription. This article presents a comprehensive review of the progress in applying AI across four dimensions: multiscale data in TCM, TCM research and development, TCM diagnosis and treatment, and LLMs. In summary, the application of AI technology in the modernization of TCM is expected to motivate researchers to achieve a deeper understanding of state-of-the-art applications in data-driven TCM complex systems, fundamental scientific research, and precision medicine, thereby bringing more opportunities and innovations for the modernization of TCM.

Ähnliche Arbeiten