XiangShu Omics: A Framework Based on the Spatio–Temporal Field Rhythms of Human Life and Systems Intelligence Methodology

This review is the first to systematically propose and construct XiangShu (image–number) omics (XSO) that inherits the ecological values, epistemology, and methodology of traditional Chinese medicine (TCM), and absorbs modern mathematical sciences. We reposition XSO as a “promising research paradigm” rather than a definitive solution, serving as an exploratory computational bridge to address the epistemological chasm. By integrating the four diagnostic methods of TCM with cutting-edge multimodal technologies spanning acoustics, optics, thermodynamics, mechanics, electromagnetism, and magnetism, Xiang omics is established based on multiscale phenotypic data ranging from astronomical and geographical parameters to human macro-, meso-, and micro-level physiological characteristics. The Xiang data are then computed into Shu (number) Omics using classical TCM numerology and modern artificial intelligence. Shu omics contains two tiers of models. The small model aims to elucidate Yin–Yang dynamics conceptualized as a mathematical classification model; the progression from pre-disease to disease to recovery modeled via Shannon entropy-based predictive frameworks; the five-element relationships represented through complex functional equations. The large model integrates large language models (LLMs) based on the classical therapeutic logic of principle–method–formula–medicine, such as the Shu-Zhi Qihuang large model. Both models work together to support intelligent reasoning and system-level knowledge synthesis. Driven by the dual engines of data-intensive and experience-driven “blind computation” and “directed inference,” XSO enables a panoramic decoding of human life rhythms across temporal, spatial, and field dimensions, thereby inform clinical decision-making and precision-oriented interventions in TCM.