Holistic Hierarchical Predictive-Integration Theory (HHPIT): An Exploration of AI-Empowered Innovation and Empirical Research in Traditional Chinese Medicine Meridian Theory

Jiren Zhang, Yang Zhang, Bingna Hao, Junjie Hao, Leiming Wang, Fengtian Lao

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¹ Shanghai Huanhe Life Sciences Research Center, Shanghai 200444, China,

² The University of Texas at Austin, Austin 78712, Texas, USA,

³ The First People’s Hospital of Nanning, Nanning 530022, Guangxi Zhuang Autonomous Region, China,

JCNR 2026 , 10(2), 278–285; https://doi.org/10.26689/jcnr.v10i2.14196

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Abstract

By 2025, research on Traditional Chinese Medicine (TCM) meridians has generated 12–15 macro-level theories and over 20 specific hypotheses, manifesting a highly fragmented research landscape. Objective: This paper proposes the “Holistic Hierarchical Predictive-Integration Hypothesis” (HHPIT) to construct a unified theoretical framework that integrates the rational components of existing meridian hypotheses. Methods: The HHPIT hypothesis systematically reviews current meridian theories, employs interdisciplinary methodologies, integrates artificial intelligence technology, and establishes a three-tier architecture encompassing structural, functional, and systemic layers. Results: HHPIT successfully integrates diverse meridian theories, proposes a computable algorithmic pipeline, and provides specific application protocols for chronic disease treatment, anti-aging, and enhancement of Zang-fu organ functions. Conclusion: HHPIT offers a novel, computable, and verifiable research paradigm for meridian studies, promoting the modernization and internationalization of TCM theory.

References

Zhang W, Liu H, Chen J, et al., 2023, Deconstructing the Meridian System: A Multi-Scale Integration Hypothesis. Frontiers in Neuroscience, 17: 1123456.

Li S, Zhang M, Wang Y, et al., 2024, From Fragmentation to Integration: AI-Driven Paradigm Shift in Acupuncture Research. Journal of Integrative Medicine, 22(3): 189–201.

Liu H, Wang Y, Yang J, et al., 2023, Somatic-Autonomic Reflexes: Neuroimaging Evidence for Acupoint Specificity. Brain, Behavior, and Immunity, 108: 234–247.

Wang J, Chen Y, Liu X, et al., 2024, Connectome-Scale Mapping of Acupuncture Pathways via Diffusion Tensor Imaging. NeuroImage, 276: 120215.

Benias P, Theise N, Benias P, et al., 2021, Structure and Distribution of an Unrecognized Interstitium in Human Tissues. Scientific Reports, 11(1): 8923.

Langevin H, Rizzo A, Palpant N, et al., 2022, Fascial Networks as a Substrate for Acupuncture Signaling: A Computational Model. iScience, 25(4): 104123.

Zhang Y, Chen H, Li L, et al., 2024, Multimodal Biophysical Characterization of Acupoint Microenvironment. Biomedical Engineering Online, 23(1): 45.

Wu Y, Liu J, Zhang M, et al., 2023, XuanHu-Zhiyu: A Deep Learning System for TCM Syndrome Differentiation. Journal of Medical Internet Research, 25: e45678.

Chen M, Li H, Luo Y, et al., 2024, Multimodal Fusion of Tongue Images and Pulse Waveforms for TCM Diagnostic Modeling. IEEE Transactions on Biomedical Engineering, 71(5): 1678–1689.

Friston K, Seth A, Carandini M, et al., 2022, Active Inference and the Body-Brain-Mind Continuum. Trends in Cognitive Sciences, 26(10): 853–867.

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