{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T18:18:49Z","timestamp":1761848329487,"version":"build-2065373602"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3624887","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T17:59:54Z","timestamp":1761242394000},"page":"183225-183241","source":"Crossref","is-referenced-by-count":0,"title":["MGPRAG: Enhancing Medical Large Language Models via Precision Retrieval-Augmented Generation"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5452-1500","authenticated-orcid":false,"given":"Yanwen","family":"Shen","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3265-8355","authenticated-orcid":false,"given":"Dahong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0407-4327","authenticated-orcid":false,"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha, China"}]},{"given":"Lei","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha, China"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"GPT-4o system card","author":"Hurst","year":"2024","journal-title":"arXiv:2410.21276"},{"key":"ref2","article-title":"DeepSeek-V3 technical report","volume-title":"arXiv:2412.19437","author":"Liu","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3402809"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3476336"},{"key":"ref5","article-title":"Data cleaning using large language models","author":"Zhang","year":"2024","journal-title":"arXiv:2410.15547"},{"key":"ref6","article-title":"A paradigm shift: The future of machine translation lies with large language models","author":"Lyu","year":"2023","journal-title":"arXiv:2305.01181"},{"key":"ref7","article-title":"Large language models on wikipedia-style survey generation: An evaluation in NLP concepts","author":"Gao","year":"2023","journal-title":"arXiv:2308.10410"},{"key":"ref8","article-title":"Leveraging large language models for concept graph recovery and question answering in NLP education","author":"Yang","year":"2024","journal-title":"arXiv:2402.14293"},{"key":"ref9","first-page":"3929","article-title":"Retrieval augmented language model pre-training","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"1","author":"Guu"},{"key":"ref10","article-title":"Knowledge-tuning large language models with structured medical knowledge bases for reliable response generation in Chinese","author":"Wang","year":"2023","journal-title":"arXiv:2309.04175"},{"key":"ref11","article-title":"Creating trustworthy LLMs: Dealing with hallucinations in healthcare AI","author":"Aurangzeb Ahmad","year":"2023","journal-title":"arXiv:2311.01463"},{"key":"ref12","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lewis"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.813"},{"key":"ref14","article-title":"Retrieval-augmented generation for large language models: A survey","author":"Gao","year":"2023","journal-title":"arXiv:2312.10997"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2022.102110"},{"key":"ref16","article-title":"Context embeddings for efficient answer generation in RAG","author":"Rau","year":"2024","journal-title":"arXiv:2407.09252"},{"key":"ref17","article-title":"Lost in the middle: How language models use long contexts","author":"Liu","year":"2023","journal-title":"arXiv:2307.03172"},{"key":"ref18","article-title":"How well do LLMs cite relevant medical references? An evaluation framework and analyses","author":"Wu","year":"2024","journal-title":"arXiv:2402.02008"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1282"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6364"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017208"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1226"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.99"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.631"},{"key":"ref25","article-title":"Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph","author":"Sun","year":"2024","journal-title":"arXiv:2407.07697"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae560"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.296"},{"key":"ref28","article-title":"Learn to refuse: Making large language models more controllable and reliable through knowledge scope limitation and refusal mechanism","author":"Cao","year":"2023","journal-title":"arXiv:2311.01041"},{"key":"ref29","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wei"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.40895"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac409"},{"key":"ref32","article-title":"BioMedGPT: Open multimodal generative pre-trained transformer for BioMedicine","author":"Luo","year":"2023","journal-title":"arXiv:2308.09442"},{"key":"ref33","article-title":"Check your facts and try again: Improving large language models with external knowledge and automated feedback","author":"Peng","year":"2023","journal-title":"arXiv:2302.12813"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17796"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006095"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1243"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.107866"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.70092"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad496"},{"key":"ref41","first-page":"148","article-title":"MedConQA: Medical conversational question answering system based on knowledge graphs","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process., Syst. Demonstrations","author":"Xia"},{"key":"ref42","first-page":"7888","article-title":"CBLUE: A Chinese biomedical language understanding evaluation benchmark","volume-title":"Proc. 60th Annu. Meeting Assoc. Comput. Linguistics","author":"Zhang"},{"key":"ref43","article-title":"From local to global: A graph RAG approach to query-focused summarization","author":"Edge","year":"2024","journal-title":"arXiv:2404.16130"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.861"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"ref46","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"arXiv:1301.3781"},{"key":"ref47","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref48","article-title":"Qwen2.5 technical report","volume-title":"arXiv:2412.15115","author":"Yang","year":"2024"},{"key":"ref49","article-title":"HuatuoGPT-II, one-stage training for medical adaption of LLMs","author":"Chen","year":"2023","journal-title":"arXiv:2311.09774"},{"key":"ref50","article-title":"BERTScore: Evaluating text generation with BERT","author":"Zhang","year":"2019","journal-title":"arXiv:1904.09675"},{"key":"ref51","article-title":"MatchBench: An evaluation of feature matchers","author":"Bian","year":"2018","journal-title":"arXiv:1808.02267"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2023.29.4.315"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01074-z"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s44387-025-00011-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11215754.pdf?arnumber=11215754","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T18:03:35Z","timestamp":1761847415000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11215754\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3624887","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}