Multimodal and data-driven approaches in acupuncture research: methods, applications, and challenges

Abstract

Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods. This review summarizes data-driven approaches in acupuncture research within a framework encompassing intervention, response, and contextual data. We discuss causal inference, artificial intelligence, text mining, and integrative analysis, along with their applications in efficacy evaluation, outcome prediction, mechanistic investigation, and clinical decision support. These approaches shift the focus of acupuncture research from population-level average effects toward individualized clinical decision-making by enabling the analysis of treatment heterogeneity and underlying mechanisms. However, current research remains limited by inadequate data standardization, insufficient external validation, and limited model interpretability. Despite these challenges, data-driven approaches offer substantial promise for advancing more rigorous and personalized acupuncture research.

Keywords

Acupuncture / Causal inference / Clinical decision support / Data-driven / Deep learning / Machine learning / Mechanistic studies / Multimodal data

References

[1] Vickers AJ, Vertosick EA, Lewith G, et al. Acupuncture for chronic pain: update of an individual patient data meta—analysis. J Pain 2018; 19: 455-474.

[2] Lu L, Zhang Y, Tang X, et al. Evidence on acupuncture therapies is underused in clinical practice and health policy. BMJ 2022; 376: 5.

[3] Cui J, Song W, Jin Y, et al. Research progress on the mechanism of the acupuncture regulating neuro—endocrine—immune network system. Vet Sci 2021; 8: 17.

[4] Han Y, Yi S, Lee Y, et al. Quantification of the parameters of twisting—rotating acupuncture manipulation using a needle force measurement system. Integr Med Res 2015; 4: 57-65.

[5] Liu X, Gong T. Artificial intelligence and evidence—based research will promote the development of traditional medicine. Acupunct Herb Med 2024; 4: 134-135.

[6] Witt CM, Graca S, Lee Y. Artificial intelligence and acupuncture: a data—driven synergy. J Integr Complement Med 2024; 30: 316-318.

[7] Bao Y, Ding H, Zhang Z, et al. Intelligent acupuncture: data—driven revolution of traditional Chinese medicine. Acupunct Herb Med 2023; 3: 271-284.

[8] Hou G, Dong B, Yu B, et al. Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine. Front Med (Lausanne) 2025; 12: 1633416.

[9] Lyu R, Gao M, Yang H, et al. Stimulation parameters of manual acupuncture and their measurement. Evid Based Complement Alternat Med 2019; 2019: 1725936.

[10] Langevin HM, Churchill DL, Fox JR, et al. Biomechanical response to acupuncture needling in humans. J Appl Physiol 2001; 91: 2471-2478.

[11] Wang B, Xu L, Yang H, et al. Manual acupuncture for neuromusculoskeletal disorders: the selection of stimulation parameters and corresponding effects. Front Neurosci 2023; 17: 1096339.

[12] Yoon D, Lee I, Chae Y. Identifying dose components of manual acupuncture to determine the dose—response relationship of acupuncture treatment: a systematic review. Am J Chin Med 2022; 50: 653-671.

[13] Zhu M, Liu D, Pei J, et al. An acupuncture manipulation classification system based on three—axis attitude sensor and computer vision. Zhen Ci Yan Jiu 2023; 48: 1274-1281.

[14] Duan Y, Zhao P, Liu S, et al. Patient—reported outcomes and acupuncture—related adverse events are overlooked in acupuncture randomised controlled trials: a cross—sectional meta—epidemiological study. BMJ Evid Based Med 2026; 31: 83-92.

[15] Wang Z, Chen L, Jiang T, et al. Research status and trends of functional magnetic resonance imaging technology in the field of acupuncture: a bibliometric analysis over the past two decades. Front Neurosci 2025; 19: 1489049.

[16] Lee S, Lee MS, Choi J, et al. Acupuncture and heart rate variability: a systematic review. Auton Neurosci 2010; 155: 5-13.

[17] Jia J, Yu Y, Deng J, et al. A review of Omics research in acupuncture: the relevance and future prospects for understanding the nature of meridians and acupoints. J Ethnopharmacol 2012; 140: 594-603.

[18] Berisha V, Krantsevich C, Hahn PR, et al. Digital medicine and the curse of dimensionality. NPJ Digit Med 2021; 4: 153.

[19] Sun S, Wei H, Zhu R, et al. Biology of the tongue coating and its value in disease diagnosis. Complement Med Res 2018; 25: 191-197.

[20] de Sa Ferreira A, Lopes AJ. Pulse waveform analysis as a bridge between pulse examination in Chinese medicine and cardiology. Chin J Integr Med 2013; 19: 307-314.

[21] AlSaad R, Abd—Alrazaq A, Boughorbel S, et al. Multimodal large language models in health care: applications, challenges, and future outlook. J Med Internet Res 2024; 26: e59505.

[22] Rijnhart JJM, Twisk JWR, Valente MJ, et al. Time lags and time interactions in mixed effects models impacted longitudinal mediation effect estimates. J Clin Epidemiol 2022; 151: 143-150.

[23] Yang J, Wang L, Zou X, et al. Effect of acupuncture for postprandial distress syndrome: a randomized clinical trial. Ann Intern Med 2020; 172: 777-785.

[24] Lu L, Chen C, Chen Y, et al. Effect of acupuncture for methadone reduction: a randomized clinical trial. Ann Intern Med 2024; 177: 1039-1047.

[25] Ghosh P, Albert PS. A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial. Comput Stat Data Anal 2009; 53: 699-706.

[26] Lu L, An J, Chen H, et al. A series of N—of—1 trials for traditional Chinese medicine using a Bayesian method: study rationale and protocol. Evid Based Complement Alternat Med 2021; 2021: 9976770.

[27] Zhang C, Mayo MS, Wick JA, et al. Designing and analyzing clinical trials for personalized medicine via Bayesian models. Pharm Stat 2021; 20: 573-596.

[28] Lindberg V, Baak J. The majority of Norwegian patients with treatment—resistant chronic pain regained normal national health standards within 12 months after De—Qi acupuncture—a prospective observational propensity score matched study. Front Pain Res (Lausanne) 2025; 6: 1521466.

[29] Ng DQ, Lee S, Lee RT, et al. Real—world impact of acupuncture on analgesics and healthcare resource utilization in breast cancer survivors with pain. BMC Med 2024; 22: 394.

[30] Tleyjeh IM, Kashour T, Mandrekar J, et al. Overlooked shortcomings of observational studies of interventions in coronavirus disease 2019: an illustrated review for the clinician. Open Forum Infect Dis 2021; 8: ofab317.

[31] Shiba K, Kawahara T. Using propensity scores for causal inference: pitfalls and tips. J Epidemiol 2021; 31: 457-463.

[32] Hernan MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 2016; 183: 758-764.

[33] Habibdoust A, Zuo H, Koopman RJ, et al. Target trial emulation in hypertension research: a scoping review of current applications and methodological practices. J Hypertens 2026; 44: 37-48.

[34] Lu TT, Wu CE, Huang TH, et al. Integrating Chinese herbal medicine in advanced lung cancer: a multicenter real—world study using target trial emulation. Phytomedicine 2025; 148: 157239.

[35] Wang X, Hu Y, Huan J, et al. Effectiveness of Xuanshen Yishen decoction on intensive blood pressure control: emulation of a randomized target trial using real—world data. Chin J Integr Med 2025; 31: 677-684.

[36] Tennant PWG, Murray EJ, Arnold KF, et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Int J Epidemiol 2021; 50: 620-632.

[37] Fei Y, Cao H, Xia R, et al. Methodological challenges in design and conduct of randomised controlled trials in acupuncture. BMJ 2022; 376: e064345.

[38] Ma Z, Lu Y, Wu J, et al. Acupuncture induces reduction in limbic—cortical feedback of a neuralgia rat model: a dynamic causal modeling study. Neural Plast 2020; 2020: 5052840.

[39] Friston KJ, Preller KH, Mathys C, et al. Dynamic causal modeling revisited. Neuroimage 2019; 199: 730-744.

[40] Rahmani AM, Yousefpoor E, Yousefpoor MS, et al. Machine learning (ML) in medicine: review, applications, and challenges. Mathematics (Basel) 2021; 9: 2970.

[41] Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380: 1347-1358.

[42] Yin T, Zheng H, Ma T, et al. Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine. EPMA J 2022; 13: 137-147.

[43] Chen L, Yin T, He Z, et al. Deqi sensation to predict acupuncture effect on functional dyspepsia: a machine learning study. Evid Based Complement Alternat Med 2022; 2022: 4824575.

[44] Fu J, Cai X, Huang S, et al. Predicting acupuncture efficacy for major depressive disorder using baseline clinical variables: a machine learning study. J Psychiatr Res 2023; 168: 64-70.

[45] Tang Y, Hu S, Xu Y, et al. Clinical efficacy of DSA—based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke. Chin Med 2024; 19: 13.

[46] Hyun S, Lee H, Park W. Individual—specific postural discomfort prediction using decision tree models. Appl Ergon 2024; 118: 104282.

[47] Huang J, Zhao L, Xie Y, et al. Investigating the analgesic mechanisms of acupuncture for cancer pain: insights from multimodal bioelectrical signal analysis. J Pain Res 2025; 18: 1435-1450.

[48] Ong SS, Tang T, Xu L, et al. Research on the mechanism of core acupoints in electroacupuncture for functional constipation based on data mining and network acupuncture. Front Med (Lausanne) 2024; 11: 1482066.

[49] Yang K, Yu Z, Su X, et al. PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases. Chin Med 2024; 19: 144.

[50] Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19: 1236-1246.

[51] Khan MNA, Ghafoor U, Yoo H, et al. Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional—near infrared spectroscopy study. Neural Regen Res 2022; 17: 1850-1856.

[52] Zhuang Q, Gan S, Zhang L. Human—computer interaction based health diagnostics using ResNet34 for tongue image classification. Comput Methods Programs Biomed 2022; 226: 107096.

[53] Zhang M, Li Y, Shi Z. Development of an abdominal acupoint localization system based on AI deep learning. Zhongguo Zhen Jiu 2025; 45: 391-396.

[54] Malekroodi HS, Seo S, Choi J, et al. Real—time location of acupuncture points based on anatomical landmarks and pose estimation models. Front Neurorobot 2024; 18: 1484038.

[55] Yeh W, Kuo C, Chen J, et al. Pioneering data processing for convolutional neural networks to enhance the diagnostic accuracy of traditional Chinese medicine pulse diagnosis for diabetes. Bioengineering (Basel) 2024; 11: 561.

[56] Li K, Wang J, Li S, et al. Latent characteristics and neural manifold of brain functional network under acupuncture. IEEE Trans Neural Syst Rehabil Eng 2022; 30: 758-769.

[57] Gong C, Jing C, Chen X, et al. Generative AI for brain image computing and brain network computing: a review. Front Neurosci 2023; 17: 18.

[58] Li R, Pan Y, Wu S, et al. A feature—aware approach to acupoint compatibility prediction using residual graph attention networks and matrix factorization. IEEE J Biomed Health Inform 2025; 29: 3750-3761.

[59] Han X, Xie X, Zhao R, et al. Calculating the similarity between prescriptions to find their new indications based on graph neural network. Chin Med 2024; 19: 11.

[60] Liu H, Han C, Xiong J, et al. Automatic labeling and extraction of terms in natural language processing in acupuncture clinical literature. Zhongguo Zhen Jiu 2022; 42: 327-331.

[61] Zhang T, Huang Z, Wang Y, et al. Information extraction from the text data on traditional Chinese medicine: a review on tasks, challenges, and methods from 2010 to 2021. Evid Based Complement Alternat Med 2022; 2022: 1679589.

[62] Lee S, Kim C, Lee I, et al. Network analysis of acupuncture points used in the treatment of low back pain. Evid Based Complement Alternat Med 2013; 2013: 402180.

[63] Chen Z, Wang H, Li C, et al. Large language models in traditional Chinese medicine: a systematic review. Acupunct Herb Med 2025; 5: 57-67.

[64] Liu Y, Yuan Y, Yan K, et al. Evaluating the role of large language models in traditional Chinese medicine diagnosis and treatment recommendations. NPJ Digit Med 2025; 8: 12.

[65] Lim J, Li J, Zhou M, et al. Machine learning research trends in traditional Chinese medicine: a bibliometric review. Int J Gen Med 2024; 17: 5397-5414.

[66] Subramanian I, Verma S, Kumar S, et al. Multi—omics data integration, interpretation, and its application. Bioinform Biol Insights 2020; 14: 1177932219899051.

[67] Cao B, Li Y, Lin M, et al. Integrated analysis of metabolomic and gut microbiota reveals idiosyncratic drug—induced liver injury resulting from the combined administration of bavachin and icariside II. Acupunct Herb Med 2024; 4: 222-233.

[68] Chen Y, Fan B, Zeng J, et al. Single—cell RNA transcriptomics and multi—omics analyses reveal the clinical effects of acupuncture on methadone reduction. Research (Wash D C) 2025; 8: 17.

[69] Yang Y, Ge F, Luo C, et al. Inhibition of hepatitis B virus through PPAR—JAK/STAT pathway modulation by electroacupuncture and tenofovir disoproxil fumarate combination therapy. Int Immunopharmacol 2024; 143: 13.

[70] Calhoun VD, Sui J. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging 2016; 1: 230-244.

[71] Zhu D, Zhang T, Jiang X, et al. Fusing DTI and fMRI data: a survey of methods and applications. Neuroimage 2014; 102 Pt 1: 184-191.

[72] Cao J, Tu Y, Wilson G, et al. Characterizing the analgesic effects of real and imagined acupuncture using functional and structure MRI. Neuroimage 2020; 221: 117176.

[73] Cui C, Yang H, Wang Y, et al. Deep multimodal fusion of image and non—image data in disease diagnosis and prognosis: a review. Prog Biomed Eng (Bristol) 2023; 5(2): 022001.

[74] Han Y, Zeng X, Hua L, et al. The fusion of multi—omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders. Microbiome 2024; 12: 12.

[75] Kong J. Electroacupuncture for treating chronic low—back pain: preliminary research results. Med Acupunct 2020; 32: 396-397.

[76] Li K, Wang J, Hu Z, et al. Gating attractor dynamics of frontal cortex under acupuncture via recurrent neural network. IEEE J Biomed Health Inform 2022; 26: 3836-3847.

[77] Rao W, Xu M, Wang H, et al. Acupuncture state detection at Zusanli (ST—36) based on scalp EEG and transformer. IEEE J Biomed Health Inform 2025; 29: 4023-4034.

[78] Guo X, Wang J. Low—dimensional dynamics of brain activity associated with manual acupuncture in healthy subjects. Sensors (Basel) 2021; 21: 7432.

[79] Liu Z, Yang T, Wang J, et al. Tianyi: a traditional Chinese medicine all—rounder language model and its real—world clinical practice. Inf Fusion 2026; 126: 103663.

[80] Zhang J, Ji C, Zhai X, et al. Global trends and hotspots in research on acupuncture for stroke: a bibliometric and visualization analysis. Eur J Med Res 2023; 28: 359.

[81] Gao Z, Cui M, Xu C, et al. Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study. Ann Med 2025; 57: 2548388.

[82] Zhu L, Liu S, Fang J, et al. Predicting acupuncture efficacy in chronic prostatitis/chronic pelvic pain syndrome: a study on model development and result visualization. Urol Int 2024; 108: 500-507.

[83] Wang H, Lengerich BJ, Aragam B, et al. Precision Lasso: accounting for correlations and linear dependencies in high—dimensional genomic data. Bioinformatics 2019; 35: 1181-1187.

[84] Liu Y, Tang Y, Li Z, et al. Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data. Front Neurol 2024; 15: 1441886.

[85] Dong Y, Fan B, Yan E, et al. Decision tree model based prediction of the efficacy of acupuncture in methadone maintenance treatment. Front Neurol 2022; 13: 956255.

[86] Yan J, Cai J, Xu Z, et al. Tongue crack recognition using segmentation based deep learning. Sci Rep 2023; 13: 11.

[87] Quanyu E. Pulse signal analysis based on deep learning network. Biomed Res Int 2022; 2022: 11.

[88] Yu H, Zeng F, Liu D, et al. Neural manifold decoder for acupuncture stimulations with representation learning: an acupuncture—brain interface. IEEE J Biomed Health Inform 2025; 29: 4147-4160.

[89] Li Y, Peng X, Li J, et al. Relation extraction using large language models: a case study on acupuncture point locations. J Am Med Inform Assoc 2024; 31: 2622-2631.

[90] Wen J, Liu D, Xie Y, et al. AcuGPT—Agent: an LLM—powered intelligent system for acupuncture—based infertility treatment. Neurocomputing 2025; 652: 131116.

[91] Wang Y, Shi X, Efferth T, et al. Artificial intelligence—directed acupuncture: a review. Chin Med 2022; 17: 80.

[92] Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021; 139: 4-15.

[93] Zhang J, Dong E, Liu L, et al. Traditional Chinese Medicine + artificial intelligence: Wuzhen consensus. Acupunct Herb Med 2025; 5: 134-135.