{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:49:36Z","timestamp":1774046976150,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762019"],"award-info":[{"award-number":["61762019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61862051"],"award-info":[{"award-number":["61862051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62062001"],"award-info":[{"award-number":["62062001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s44443-025-00123-1","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T10:50:55Z","timestamp":1751539855000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DiagX-DT: diagnostic exclusion reasoning with dialectical thinking for traditional Chinese medicine in large language models"],"prefix":"10.1007","volume":"37","author":[{"given":"Guoxia","family":"Nie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoyun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"123_CR1","unstructured":"AI@Meta (2024) Llama 3 model card"},{"key":"123_CR2","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s11606-019-04867-1","volume":"34","author":"PA Bergl","year":"2019","unstructured":"Bergl PA, Manesh R, Basel D, Olson AP (2019) Keeping a flexible differential diagnosis: an exercise in clinical reasoning. J Gener Int Med 34:1063\u20131068","journal-title":"J Gener Int Med"},{"key":"123_CR3","unstructured":"Chen Y, Sun P, Li X, Chu X (2025) Mrd-rag: enhancing medical diagnosis with multi-round retrieval-augmented generation. arXiv:2504.07724"},{"issue":"1","key":"123_CR4","doi-asserted-by":"publisher","first-page":"101019","DOI":"10.1016\/j.imr.2023.101019","volume":"13","author":"Z Chen","year":"2024","unstructured":"Chen Z, Zhang D, Liu C, Wang H, Jin X, Yang F, Zhang J (2024) Traditional chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning. Integr Med Res 13(1):101019","journal-title":"Integr Med Res"},{"key":"123_CR5","unstructured":"DeepSeek-AI (2025) Deepseek-r1: incentivizing reasoning capability in llms via reinforcement learning"},{"key":"123_CR6","unstructured":"Ding H, Pang L, Wei Z, Shen H, Cheng X (2024). Retrieve only when it needs: adaptive retrieval augmentation for hallucination mitigation in large language models. arXiv:2402.10612"},{"key":"123_CR7","unstructured":"GLM T, Zeng A, Xu B, Wang B, Zhang C, Yin D, Rojas D, Feng G, Zhao H, Lai H, Yu H, Wang H, Sun J, Zhang J, Cheng J, Gui J, Tang J, Zhang J, Li J, Zhao L, Wu L, Zhong L, Liu M, Huang M, Zhang P, Zheng Q, Lu R, Duan S, Zhang S, Cao S, Yang S, Tam WL, Zhao W, Liu X, Xia X, Zhang X, Gu X, Lv X, Liu X, Liu X, Yang X, Song X, Zhang X, An Y, Xu Y, Niu Y, Yang Y, Li Y, Bai Y, Dong Y, Qi Z, Wang Z, Yang Z, Du Z, Hou Z, Wang Z (2024) Chatglm: a family of large language models from glm-130b to glm-4 all tools"},{"issue":"10","key":"123_CR8","doi-asserted-by":"publisher","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","volume":"7","author":"E Goh","year":"2024","unstructured":"Goh E, Gallo R, Hom J, Strong E, Weng Y, Kerman H, Cool JA, Kanjee Z, Parsons AS, Ahuja N et al (2024) Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open 7(10):e2440969\u2013e2440969","journal-title":"JAMA Netw Open"},{"key":"123_CR9","unstructured":"He J, Guo Y, Lam LK, Leung W, He L, Jiang Y, Wang CC, Xing G, Chen H (2025) Opentcm: a graphrag-empowered llm-based system for traditional chinese medicine knowledge retrieval and diagnosis. arXiv:2504.20118"},{"issue":"9","key":"123_CR10","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1093\/jamia\/ocae087","volume":"31","author":"R Hua","year":"2024","unstructured":"Hua R, Dong X, Wei Y, Shu Z, Yang P, Hu Y, Zhou S, Sun H, Yan K, Yan X et al (2024) Lingdan: enhancing encoding of traditional chinese medicine knowledge for clinical reasoning tasks with large language models. J Am Med Inf Assoc 31(9):2019\u20132029","journal-title":"J Am Med Inf Assoc"},{"key":"123_CR11","unstructured":"Kang Y, Chang Y, Fu J, Wang Y, Wang H, Zhang W (2023) Cmlm-zhongjing: Large language model is good story listener. https:\/\/github.com\/pariskang\/CMLM-ZhongJing"},{"key":"123_CR12","first-page":"22199","volume":"35","author":"T Kojima","year":"2022","unstructured":"Kojima T, Gu SS, Reid M, Matsuo Y, Iwasawa Y (2022) Large language models are zero-shot reasoners. Adv Neural Inf Process Syst 35:22199\u201322213","journal-title":"Adv Neural Inf Process Syst"},{"key":"123_CR13","unstructured":"Laleh AR, Ahmadabadi MN (2024) A survey on enhancing reinforcement learning in complex environments: insights from human and llm feedback. arXiv:2411.13410"},{"key":"123_CR14","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, K\u00fcttler H, Lewis M, Yih W-T, Rockt\u00e4schel T et al (2020) Retrieval-augmented generation for knowledge-intensive nlp tasks. Adv Neural Inf Process Syst 33:9459\u20139474","journal-title":"Adv Neural Inf Process Syst"},{"key":"123_CR15","doi-asserted-by":"crossref","unstructured":"Li Y, Li Z, Zhang K, Dan R, Jiang S, Zhang Y (2023) Chatdoctor: a medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge. Cureus 15(6)","DOI":"10.7759\/cureus.40895"},{"key":"123_CR16","unstructured":"Liu Y, Luo S, Zhong Z, Wu T, Zhang J, Ou P, Liang Y, Liu L, Pan H (2025) Hengqin-ra-v1: advanced large language model for diagnosis and treatment of rheumatoid arthritis with dataset based traditional chinese medicine. arXiv:2501.02471"},{"key":"123_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Peng X, Zhang X, Liu W, Yin J, Cao J, Du T (2024) Ra-isf: learning to answer and understand from retrieval augmentation via iterative self-feedback. In: Findings of the association for computational linguistics ACL 2024, pp 4730\u20134749","DOI":"10.18653\/v1\/2024.findings-acl.281"},{"key":"123_CR18","unstructured":"OpenAI (2025) Introducing chatgpt. Accessed on 5-April-2025"},{"key":"123_CR19","doi-asserted-by":"crossref","unstructured":"Ren Y, Luo X, Wang Y, Li H, Zhang H, Li Z, Lai H, Li X, Ge L, Estill J et al (2025) Large language models in traditional chinese medicine: a scoping review. J Evid -Based Med 18(1):e12658","DOI":"10.1111\/jebm.12658"},{"key":"123_CR20","doi-asserted-by":"crossref","unstructured":"Sandner E, Hu B, Simiceanu A, Fontana L, Jakovljevic I, Henriques A, Wagner A, G\u00fctl C (2024) Screening automation for systematic reviews: a 5-tier prompting approach meeting cochrane\u2019s sensitivity requirement. In: 2024 2nd International conference on foundation and large language models (FLLM). IEEE, pp 150\u2013159","DOI":"10.1109\/FLLM63129.2024.10852425"},{"key":"123_CR21","unstructured":"Sen R, Roychowdhury S, Soman S, Ranjani H, Mohanty S (2025) Knowledge distillation of domain-adapted llms for question-answering in telecom. arXiv:2504.20000"},{"key":"123_CR22","doi-asserted-by":"crossref","unstructured":"Singh K, Gupta JK, Jain D, Kumar S, Singh T, Saha S (2024). Exploring the ancient wisdom and modern relevance of chinese medicine: a comprehensive review. Pharmacol Res -Modern Chin Med 100448","DOI":"10.1016\/j.prmcm.2024.100448"},{"issue":"41","key":"123_CR23","doi-asserted-by":"publisher","first-page":"16844","DOI":"10.1039\/D4SC04107K","volume":"15","author":"Z Song","year":"2024","unstructured":"Song Z, Chen G, Chen CY-C (2024) Ai empowering traditional chinese medicine? Chem Sci 15(41):16844\u201316886","journal-title":"Chem Sci"},{"key":"123_CR24","doi-asserted-by":"crossref","unstructured":"Su X, Gu Y (2024) Implementing retrieval-augmented generation (rag) for large language models to build confidence in traditional chinese medicine","DOI":"10.31219\/osf.io\/ns2v3"},{"key":"123_CR25","doi-asserted-by":"publisher","first-page":"108290","DOI":"10.1016\/j.compbiomed.2024.108290","volume":"172","author":"Y Tan","year":"2024","unstructured":"Tan Y, Zhang Z, Li M, Pan F, Duan H, Huang Z, Deng H, Yu Z, Yang C, Shen G et al (2024) Medchatzh: a tuning llm for traditional chinese medicine consultations. Comput Biol Med 172:108290","journal-title":"Comput Biol Med"},{"key":"123_CR26","unstructured":"Wang H, Liu C, Xi N, Qiang Z, Zhao S, Qin B, Liu T (2023) Huatuo: tuning llama model with chinese medical knowledge. arXiv:2304.06975"},{"key":"123_CR27","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D et al (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35:24824\u201324837","journal-title":"Adv Neural Inf Process Syst"},{"key":"123_CR28","unstructured":"Wei S, Peng X, Wang Y-f, Si J, Zhang W, Lu W, Wu X, Wang Y (2024). Biancang: a traditional chinese medicine large language model. arXiv:2411.11027"},{"issue":"3","key":"123_CR29","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.ajhg.2025.02.003","volume":"112","author":"TL Wenger","year":"2025","unstructured":"Wenger TL, Scott A, Kruidenier L, Sikes M, Keefe A, Buckingham KJ, Marvin CT, Shively KM, Bacus T, Sommerland OM et al (2025) Seqfirst: building equity access to a precise genetic diagnosis in critically ill newborns. Am J Human Genet 112(3):508\u2013522","journal-title":"Am J Human Genet"},{"key":"123_CR30","doi-asserted-by":"crossref","unstructured":"Wu M, Ma T, Zhu Y, Ren H, Fu L (2024) The evolution of traditional chinese medicine as recombinant inventions. Proc Natl Acad Sci 121(46):e2400812121","DOI":"10.1073\/pnas.2400812121"},{"key":"123_CR31","doi-asserted-by":"crossref","unstructured":"Xia N (2025) Pharmaceutical industry influence, traditional medical knowledge, and the evolution of ip governance in china. In: Medicinal mandates: the intersection of chinese traditional medical knowledge and modern law. Springer, pp 159\u2013201","DOI":"10.1007\/978-981-96-4108-6_4"},{"key":"123_CR32","unstructured":"Xiong H, Wang S, Zhu Y, Zhao Z, Liu Y, Huang L, Wang Q, Shen D (2023) Doctorglm: fine-tuning your chinese doctor is not a herculean task. arXiv:2304.01097"},{"key":"123_CR33","unstructured":"Xu J, Guo Z, He J, Hu H, He T, Bai S, Chen K, Wang J, Fan Y, Dang K, Zhang B, Wang X, Chu Y, Lin J (2025) Qwen2.5-omni technical report. arXiv:2503.20215"},{"key":"123_CR34","doi-asserted-by":"crossref","unstructured":"Xu P, Wu H, Wang J, Lin R, Tan L (2024) Traditional chinese medicine case analysis system for high-level semantic abstraction: Optimized with prompt and rag. In: China health information processing conference. Springer, pp 10\u201325","DOI":"10.1007\/978-981-96-4298-4_2"},{"key":"123_CR35","unstructured":"Yan Y, Ma T, Li R, Zheng X, Shan G, Li C (2025) Jingfang: a traditional chinese medicine large language model of expert-level medical diagnosis and syndrome differentiation-based treatment. arXiv:2502.04345"},{"key":"123_CR36","doi-asserted-by":"publisher","first-page":"100158","DOI":"10.1016\/j.cmpbup.2024.100158","volume":"6","author":"G Yang","year":"2024","unstructured":"Yang G, Liu X, Shi J, Wang Z, Wang G (2024) Tcm-gpt: efficient pre-training of large language models for domain adaptation in traditional chinese medicine. Comput Methods Progr Biomed Update 6:100158","journal-title":"Comput Methods Progr Biomed Update"},{"key":"123_CR37","unstructured":"Yu Z, He L, Wu Z, Dai X, Chen J (2023) Towards better chain-of-thought prompting strategies: a survey. arXiv:2310.04959"},{"key":"123_CR38","unstructured":"Zhang H, Wang X, Meng Z, Chen Z, Zhuang P, Jia Y, Xu D, Guo W (2024a) Qibo: a large language model for traditional chinese medicine. arXiv:2403.16056"},{"key":"123_CR39","doi-asserted-by":"crossref","unstructured":"Zhang H, Wu Y, Li D, Yang S, Zhao R, Jiang Y, Tan F (2024) Balancing speciality and versatility: a coarse to fine framework for supervised fine-tuning large language model. In: Findings of the association for computational linguistics ACL 2024, pp 7467\u20137509","DOI":"10.18653\/v1\/2024.findings-acl.445"},{"key":"123_CR40","unstructured":"Zhang T, Patil SG, Jain N, Shen S, Zaharia M, Stoica I, Gonzalez JE (2024c) Raft: adapting language model to domain specific rag. In: First conference on language modeling"},{"key":"123_CR41","doi-asserted-by":"crossref","unstructured":"Zhou X, Dong X, Li C, Bai Y, Xu Y, Cheung KC, Se S, Song X, Zhang R, Zho X et\u00a0al (2024) Tcm-ftp: fine-tuning large language models for herbal prescription prediction. In: 2024 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 4092\u20134097","DOI":"10.1109\/BIBM62325.2024.10822451"},{"key":"123_CR42","doi-asserted-by":"publisher","first-page":"109887","DOI":"10.1016\/j.compbiomed.2025.109887","volume":"189","author":"Y Zhuang","year":"2025","unstructured":"Zhuang Y, Yu L, Jiang N, Ge Y (2025) Tcm-kllama: intelligent generation model for traditional chinese medicine prescriptions based on knowledge graph and large language model. Comput Biol Med 189:109887","journal-title":"Comput Biol Med"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00123-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00123-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00123-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T00:47:59Z","timestamp":1757206079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00123-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["123"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00123-1","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"21 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}],"article-number":"102"}}