Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine

• DOI: 10.1016/j.eng.2024.04.009
• Publication Date: 2024-04-26T16:36:51Z
• Authors: Jingqi Zeng; Xiaobin Jia
• Keywords: Artificial intelligence; Traditional Chinese medicine; Material basis; Systems theory; Bottom-up; TA1-2040; Engineering (General). Civil engineering (General)

This paper introduces a systems theory-driven framework to integration artificial intelligence (AI) into traditional Chinese medicine (TCM) research, enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles. Utilizing the System Function Decoding Model (SFDM), the research progresses through define, quantify, infer, and validate phases to systematically explore TCM’s material basis. It employs a dual analytical approach that combines top-down, systems theory-guided perspectives with bottom-up, elements–structure–function methodologies, provides comprehensive insights into TCM’s holistic material basis. Moreover, the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components, proposing two specific AI-driven technical applications. This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.