Evaluating the role of large language models in traditional Chinese medicine diagnosis and treatment recommendations
Jiang, H. et al. Deciphering the mechanistic impact of acupuncture on the neurovascular unit in acute ischemic stroke: Insights from basic research in a narrative review. Ageing Res. Rev. 101, 102536 (2024).
Google Scholar
Zhang, Y. et al. Efficacy of integrated rehabilitation techniques of traditional Chinese medicine for ischemic stroke: a randomized controlled trial. Am. J. Chin. Med. 41, 971–981 (2013).
Google Scholar
Hollifield, M. et al. Acupuncture for combat-related posttraumatic stress disorder: a randomized clinical trial. JAMA Psychiatry 81, 545–554 (2024).
Google Scholar
Hung, H.-H., Chen, W.-C., Chen, Y.-H., Chiu, L.-T. & Chen, H.-Y. Evaluation of the efficacy of Chinese herbal medicine and acupuncture for the prevention of mental disorders in interstitial cystitis patients. Medicine 99, e21422 (2020).
Google Scholar
Tu, J.-F. et al. Acupuncture vs Sham acupuncture for chronic sciatica from herniated disk: a randomized clinical trial. JAMA Intern. Med. 184, 1417–1424 (2024).
Google Scholar
Zhao, L. et al. Long-term effects of individualized acupuncture for chronic neck pain. Ann. Intern Med 177, 1330–1338 (2024).
Google Scholar
Xiao, Q. et al. Exploring the efficacy and safety of acupuncture versus sham acupuncture for diminished ovarian reserve: study protocol for a multicentre randomised controlled trial. BMJ Open 14, e081098 (2024).
Google Scholar
Liu, Z. et al. Effect of electroacupuncture on urinary leakage among women with stress urinary incontinence: a randomized clinical trial. JAMA 317, 2493–2501 (2017).
Google Scholar
Cohen, L. et al. Acupuncture for chronic radiation-induced xerostomia in head and neck cancer: a multicenter randomized clinical trial. JAMA Netw. Open 7, e2410421 (2024).
Google Scholar
Zhang, X., Qiu, H., Li, C., Cai, P. & Qi, F. The positive role of traditional Chinese medicine as an adjunctive therapy for cancer. Biosci. Trends (2021).
Sang, X., Wang, Z., Liu, S.-Y. & Wang, R.-L. Relationship between traditional Chinese Medicine(TCM)Constitution and TCM Syndrome in the diagnosis and treatment of chronic diseases. Chin. Med. Sci. J. 33, 114–119 (2018).
Google Scholar
Li, Y. et al. Altered effective connectivity of resting-state networks by Tai Chi Chuan in chronic fatigue syndrome patients: a multivariate Granger Causality Study. Front. Neurol. 13, 858833 (2022).
Google Scholar
Zhao, C. et al. Chinese medicine formula Huashibaidu Granule early treatment for mild COVID-19 Patients: An unblinded, cluster-randomized clinical trial. Front. Med. 8, 696976 (2021).
Tang, J.-L., Hackshaw, A., Lao, L.-X., Liu, B.-Y. & Chung, V. C.-H. Improving research on the efficacy, effectiveness, and harms of traditional Chinese medicine. Evid. -Based Complement. Alternat. Med. 2014, 657679 (2014).
Google Scholar
Ren, X. Pondering about study on subhealth state with Traditional Chinese medicine. China J. Basic Med. Tradit. Chin. Med 11, 40–41 (2005).
Yan, M. et al. Acupuncture and sleep quality among patients with Parkinson disease: a randomized clinical trial. JAMA Netw. Open 7, e2417862 (2024).
Google Scholar
Jiang, M. et al. Syndrome differentiation in modern research of traditional Chinese medicine. J. Ethnopharmacol. 140, 634–642 (2012).
Google Scholar
Coutinho, B. D. & Dulcetti, P. G. S. The Yin and Yang movement in the cosmology of Chinese medicine]. Hist. Cienc. Saude-Manguinhos 22, 797–811 (2015).
Google Scholar
Matos, L. C., Machado, J., Greten, H. & Monteiro, F. Changes of skin electrical potential in acupoints from Ren Mai and Du Mai conduits during Qigong practice: Documentation of a clinical phenomenon. J. Bodyw. Mov. Ther. 23, 713–720 (2019).
Google Scholar
Booker, A. Chinese Medicine: Contentions and Global Complexities. in (eds. Heinrich, M. & Jäger, A. K.) 293–302 (Wiley, 2015). https://doi.org/10.1002/9781118930717.ch25.
Scheid, V. Chinese Medicine in Contemporary China: Plurality and Synthesis. Durham, USA: Duke University Press (2002).
Ren, Y. et al. Large Language Models in Traditional Chinese Medicine: A Scoping Review. J. Evid. Based Med. e12658 (2024).
Omiye, J., Gui, H., Rezaei, S. J., Zou, J. & Daneshjou, R. Large language models in medicine: the potentials and pitfalls. Ann. Intern. Med. 177, 210–220 (2023).
Weissler, E. H. et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 22, 537 (2021).
Google Scholar
Telenti, A. et al. Large language models for science and medicine. Eur. J. Clin. Investig. 54, e14183 (2024).
Guo, Z. et al. Large language model for mental health: A systematic review. arXiv preprint: 2403.15401 (2024).
Abdulnazar, A., Roller, R., Schulz, S. & Kreuzthaler, M. Large language models for clinical text cleansing enhance medical concept normalization. IEEE Access 12, 147981–147990 (2024).
Savage, T., Nayak, A., Gallo, R., Rangan, E. & Chen, J. H. Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine. NPJ Digit. Med. 7 (2023).
Schuemie, M. J. et al. Standardized patient profile review using large language models for case adjudication in observational research. npj Digit. Med. 8, 1–7 (2025).
Ahmed, A., Aziz, S., Abd-alrazaq, A., AlSaad, R. & Sheikh, J. Leveraging LLMs and wearables to provide personalized recommendations for enhancing student well-being and academic performance through a proof of concept. Sci. Rep. 15, 4591 (2025).
Google Scholar
Abd-alrazaq, A. A. et al. Large language models in medical education: opportunities, challenges, and future directions. JMIR Med. Educ. 9, e48291 (2023).
Google Scholar
Shah, K. et al. Large language model prompting techniques for advancement in clinical medicine. J. Clin. Med. 13, 5101 (2024).
Google Scholar
Rodriguez, J. A., Alsentzer, E. & Bates, D. W. Leveraging large language models to foster equity in healthcare. J. Am. Med. Inform. Assoc. (2024).
Qiu, P. et al. Towards building multilingual language model for medicine. Nat. Commun. 15, 8384 (2024).
Google Scholar
Hua, R. et al. Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models. J. Am. Med Inf. Assoc. 31, 2019–2029 (2024).
Bicknell, B. T. et al. Critical analysis of ChatGPT 4 Omni in USMLE disciplines, clinical clerkships, and clinical skills. JMIR Med. Educ. (2024).
Guerra, G. A. et al. GPT-4 artificial intelligence model outperforms ChatGPT, medical students, and neurosurgery residents on neurosurgery written board-like questions. World Neurosurg. 179, e160–e165 (2023).
Google Scholar
Ali, R. et al. Performance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations. Neurosurgery (2023).
Liu, Z., Quan, Y., Lyu, X. & Alenazi, M. J. F. Enhancing clinical accuracy of medical chatbots with large language models. IEEE J. Biomed. Health Inform. PP (2024).
Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).
Google Scholar
Tripathi, S., Sukumaran, R. & Cook, T. S. Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care. J. Am. Med. Inform. Assoc. (2024).
Qu, F. et al. An evaluation survey of traditional Chinese medicine learning among international students majoring in conventional medicine: a study from a university in China. BMC Complement. Med. Ther. 21, 16 (2021).
Google Scholar
Liu, Y., Yang, Z., Cheng, J. & Fan, D. Barriers and countermeasures in developing traditional Chinese medicine in Europe. Front. Med. 10, 360–376 (2016).
Google Scholar
Kong, Q., Chen, L., Yao, J., Ding, C. & Yin, P. Feasibility and challenges of interactive AI for traditional Chinese medicine: an example of ChatGPT. Chin. Med. Cult. 7, 174 (2024).
Quttainah, M., Mishra, V., Madakam, S., Lurie, Y. & Mark, S. Cost, Usability, credibility, fairness, accountability, transparency, and explainability framework for safe and effective large language models in medical education: narrative review and qualitative study. JMIR AI 3 (2023).
Li, Y. et al. Relation extraction using large language models: a case study on acupuncture point locations. J. Am. Med. Inf. Assoc. 31, 2622–2631 (2024).
Ong, J. C. L. et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit. Health 6, e428–e432 (2024).
Google Scholar
Feng, T. et al. From Pre-training Corpora to large language models: what factors influence LLM performance in causal discovery tasks?. arXiv preprint: 2407.19638 (2024).
Wang, A., Liu, C., Yang, J. & Weng, C. Fine-tuning large language models for rare disease concept normalization. J. Am. Med. Inf. Assoc. 31, 2076–2083 (2024).
Sakai, Y. et al. Toward the evaluation of large language models considering score variance across instruction templates. arXiv preprint: 2408.12263 (2024).
Lucas, M. M., Yang, J., Pomeroy, J. K. & Yang, C. C. Reasoning with large language models for medical question answering. J. Am. Med. Inf. Assoc. 31, 1964–1975 (2024).
Sanderson, K. GPT-4 is here: what scientists think. Nature 615, 773–773 (2023).
Google Scholar
Lahat, A. et al. Assessing Generative Pretrained Transformers (GPT) in clinical decision-making: comparative analysis of GPT-3.5 and GPT-4. J. Med Internet Res 26, e54571 (2024).
Google Scholar
Fei, Y., Fan, J. & Zhou, G. Extracting fruit disease knowledge from research papers based on large language models and prompt engineering. Appl. Sci. 15, 628 (2025).
Google Scholar
Meng, X. et al. Real-World Performance of Large Language Models in Emergency Department Chest Pain Triage. 2024.04.24.24306264 Preprint at (2024).
Gong, L., Jiang, J., Chen, S. & Qi, M. A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining. Front. Genet. 14, 1272016 (2023).
Google Scholar
Karabacak, M. & Margetis, K. Embracing large language models for medical applications: opportunities and challenges. Cureus 15, e39305.
Pan, D., Guo, Y., Fan, Y. & Wan, H. Development and application of traditional Chinese medicine using AI machine learning and deep learning strategies. Am. J. Chin. Med. 1–19 (2024).
Chung, V. C. H., Ho, R. S. T., Wu, X. & Wu, J. C. Y. Incorporating traditional Chinese medicine syndrome differentiation in randomized trials: Methodological issues. Eur. J. Integr. Med. 8, 898–904 (2016).
Biao, L. Acupuncture treatment of dysphagia caused by progressive bulbar palsy: a case report Chin. Acupunct. Moxibustion 26, 531 (2006).
Peng, S. et al. Current state of research on acupuncture for the treatment of amyotrophic lateral sclerosis: A scoping review. Front. Neurol. 13, 1019156 (2022).
Google Scholar
Guo, C. et al. A survey of pharmacogenomics testing among physicians, pharmacists, and researchers from China. Front. Pharmacol. 12, 682020 (2021).
Google Scholar
Ayers, J. W. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. 183, 589–596 (2023).
Google Scholar
Sorin, V. et al. Large language model (ChatGPT) as a support tool for breast tumor board. npj Breast Cancer 9, 1–4 (2023).
World Medical Association World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 310, 2191–2194 (2013).
IBM SPSS Statistics for Macintosh, Version 29.0.2.0. IBM Corp. (2023).
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