{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:41:48Z","timestamp":1790642508583,"version":"4.1.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Chronic low back pain (CLBP) is a global health problem that seriously affects the quality of life among patients. The etiology of CLBP is complex, with non-specific symptoms and considerable heterogeneity, which poses a great challenge for diagnosis. In addition, the uncertain treatment responses as well as the potential influence of psychological and social factors further increase the difficulty of personalized decision-making in clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study proposed an innovative support framework on clinical decision, which combined large language models (LLMs) with retrieval-augmented generation (RAG) technology. Moreover, the least-to-most (LtM) prompting technology was introduced, aiming to simulate the decision-making process of senior experts thereby improving personalized treatment for CLBP. Additionally, a special CLBP-related dataset was generated to verify effectiveness of the framework, which compared the proposed model CLBP-GPT with GPT-4.0, ERNIE Bot, and DeepSeek in terms of five key indicators: accuracy, relevance, clarity, benefit, and completeness.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results showed that the CLBP-GPT model proposed in this study scored significantly better than other comparison models in all five evaluation dimensions. Specifically, the total score of CLBP-GPT was 4.40 (SD = 0.20), substantially higher than GPT-4.0 (4.03, SD = 0.48), ERNIE Bot (3.54, SD = 0.53), and DeepSeek (3.81, SD = 0.47). In terms of accuracy, the average score of CLBP-GPT was 4.38 (SD = 0.19), while the scores of other models were all below 4, indicating that CLBP-GPT could provide more accurate clinical decision-making recommendations. In addition, CLBP-GPT scored as high as 4.42 (SD = 0.19) in the completeness dimension, further demonstrating that the decision content output by the model was more comprehensive and covered more key information related to CLBP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This study not only provides new technical support for clinical decision-making in CLBP, but also introduces a powerful tool for doctors to formulate personalized and efficient treatment strategies. It is expected to improve the diagnosis and treatment of CLBP in the future.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1599241","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T05:36:35Z","timestamp":1747200995000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology"],"prefix":"10.3389","volume":"12","author":[{"given":"Rong","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuhua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuhuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.21037\/atm.2020.02.175","article-title":"Global low back pain prevalence and years lived with disability from 1990 to 2017: Estimates from the Global burden of disease study 2017.","volume":"8","author":"Wu","year":"2017","journal-title":"Ann Transl Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1097\/j.pain.0000000000001102","article-title":"Variability in negative emotions among individuals with chronic low back pain: Relationships with pain and function.","volume":"159","author":"Gerhart","year":"2018","journal-title":"Pain."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12968\/hmed.2023.0063","article-title":"The adult with low back pain: Causes, diagnosis, imaging features and management.","volume":"84","author":"Kabeer","year":"2023","journal-title":"Br J Hosp Med (Lond)."},{"key":"B4","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1002\/pmrj.13112","article-title":"Preferences for risks and benefits of treatment outcomes for chronic low back pain: Choice-based conjoint measure development and discrete choice experiment.","volume":"16","author":"Wilson","year":"2024","journal-title":"PM R"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1007\/s00586-021-06809-0","article-title":"The catastrophization effects of an MRI report on the patient and surgeon and the benefits of \u2018clinical reporting\u2019: Results from an RCT and blinded trials.","volume":"30","author":"Rajasekaran","year":"2021","journal-title":"Eur Spine J."},{"key":"B6","doi-asserted-by":"publisher","first-page":"752","DOI":"10.21037\/jss-20-492","article-title":"Trends in lumbar spinal fusion-a literature review.","volume":"6","author":"Reisener","year":"2020","journal-title":"J Spine Surg."},{"key":"B7","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.wneu.2020.11.121","article-title":"Is lumbar fusion necessary for chronic low back pain associated with degenerative disk disease? A Meta-Analysis.","volume":"146","author":"Xu","year":"2021","journal-title":"World Neurosurg."},{"key":"B8","doi-asserted-by":"publisher","first-page":"CD001431","DOI":"10.1002\/14651858.CD001431.pub5","article-title":"Decision aids for people facing health treatment or screening decisions.","volume":"4","author":"Stacey","year":"2017","journal-title":"Cochrane Database Syst Rev."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s11606-012-2077-6","article-title":"Shared decision making: A model for clinical practice.","volume":"27","author":"Elwyn","year":"2012","journal-title":"J Gen Intern Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"103261","DOI":"10.1016\/j.msksp.2025.103261","article-title":"Development of a patient decision aid for people with chronic low back pain and degenerative disc disease considering lumbar fusion: A mixed-methods study.","volume":"76","author":"Cho","year":"2025","journal-title":"Musculoskelet Sci Pract."},{"key":"B11","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jphys.2019.05.010","article-title":"People with low back pain want clear, consistent and personalised information on prognosis, treatment options and self-management strategies: A systematic review.","volume":"65","author":"Lim","year":"2019","journal-title":"J Physiother."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1177\/2374373519897176","article-title":"Patient information leaflets for lumbar spine surgery: A missed opportunity.","volume":"7","author":"Low","year":"2020","journal-title":"J Patient Exp."},{"key":"B13","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s12911-023-02105-7","article-title":"Automatic medical specialty classification based on patients\u2019 description of their symptoms.","volume":"23","author":"Mao","year":"2023","journal-title":"BMC Med Inform Decis Mak."},{"key":"B14","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s00240-024-01644-6","article-title":"Automatic kidney stone identification: An adaptive feature-weighted LSTM model based on urine and blood routine analysis.","volume":"52","author":"Zhu","year":"2024","journal-title":"Urolithiasis."},{"key":"B15","doi-asserted-by":"publisher","first-page":"e4793","DOI":"10.1002\/ejp.4793","article-title":"A multidimensional regression model for predicting recurrence in chronic low back pain.","volume":"29","author":"Huang","year":"2025","journal-title":"Eur J Pain"},{"key":"B16","doi-asserted-by":"publisher","first-page":"104522","DOI":"10.1016\/j.jpain.2024.03.018","article-title":"Impact of shared decision-making on opioid prescribing among patients with chronic pain: A retrospective cohort study.","volume":"25","author":"Licciardone","year":"2024","journal-title":"J Pain."},{"key":"B17","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future.","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1093\/jamia\/ocy072","article-title":"Conversational agents in healthcare: A systematic review.","volume":"25","author":"Laranjo","year":"2018","journal-title":"J Am Med Inform Assoc."},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.18223","article-title":"A survey of large language models.","author":"Zhao","year":"2023","journal-title":"arXiv [Preprint]"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine.","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat Med."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.48550\/arXiv.2005.14165","article-title":"Language models are few-shot learners.","volume":"33","author":"Brown","year":"2020","journal-title":"Adv Neural Information Process Syst."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","article-title":"Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine.","volume":"388","author":"Lee","year":"2023","journal-title":"N Engl J Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1001\/jama.2023.9630","article-title":"Generative AI in health care and liability risks for physicians and safety concerns for patients.","volume":"330","author":"Duffourc","year":"2023","journal-title":"JAMA."},{"key":"B24","doi-asserted-by":"publisher","first-page":"AIra2400380","DOI":"10.1056\/AIra2400380","article-title":"RAG in health care: A novel framework for improving communication and decision-making by addressing LLM limitations.","volume":"2","author":"Ng","year":"2025","journal-title":"NEJM AI."},{"key":"B25","doi-asserted-by":"publisher","first-page":"e50638","DOI":"10.2196\/50638","article-title":"Prompt engineering as an important emerging skill for medical professionals: Tutorial.","volume":"25","author":"Mesk\u00f3","year":"2023","journal-title":"J Med Internet Res."},{"key":"B26","doi-asserted-by":"publisher","first-page":"e51282","DOI":"10.2196\/51282","article-title":"Assessing GPT-4\u2019s performance in delivering medical advice: Comparative analysis with human experts.","volume":"10","author":"Jo","year":"2024","journal-title":"JMIR Med Educ."},{"key":"B27","doi-asserted-by":"publisher","first-page":"445","DOI":"10.3390\/medicina60030445","article-title":"Integrating retrieval-augmented generation with large language models in nephrology: Advancing practical applications.","volume":"60","author":"Miao","year":"2024","journal-title":"Medicina"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1117\/12.3056789","article-title":"IHILLM-RAG: A safe and private medical large language model based on intelligent hardware interaction and retrieval-augmented generation (RAG).","author":"Zhou","year":"2025","journal-title":"in Proceedings of the Fourth International Computational Imaging Conference (CITA 2024)."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1007\/s10439-023-03327-6","article-title":"Potential for GPT technology to optimize future clinical decision-making using retrieval-augmented generation.","volume":"52","author":"Wang","year":"2024","journal-title":"Ann Biomed Eng."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2205.10625","article-title":"Least-to-most prompting enables complex reasoning in large language models.","author":"Zhou","year":"2022","journal-title":"arXiv [Preprint]"},{"key":"B31","doi-asserted-by":"publisher","first-page":"e54571","DOI":"10.2196\/54571","article-title":"Assessing generative pretrained transformers (GPT) in Clinical decision-making: Comparative analysis of GPT-3.5 and GPT-4.","volume":"26","author":"Lahat","year":"2024","journal-title":"J Med Internet Res."},{"key":"B32","doi-asserted-by":"publisher","first-page":"e46885","DOI":"10.2196\/46885","article-title":"The role of ChatGPT, generative language models, and artificial intelligence in medical education: A conversation with ChatGPT and a call for papers.","volume":"9","author":"Eysenbach","year":"2023","journal-title":"JMIR Med Educ."},{"key":"B33","doi-asserted-by":"publisher","first-page":"3041","DOI":"10.1007\/s00417-023-06078-1","article-title":"Artificial intelligence-based ChatGPT chatbot responses for patient and parent questions on vernal keratoconjunctivitis.","volume":"261","author":"Rasmussen","year":"2023","journal-title":"Graefes Arch Clin Exp Ophthalmol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1007\/s11695-023-06603-5","article-title":"Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery.","volume":"33","author":"Samaan","year":"2023","journal-title":"Obes Surg"},{"key":"B35","doi-asserted-by":"publisher","first-page":"kad015","DOI":"10.1093\/jncics\/pkad015","article-title":"Using ChatGPT to evaluate cancer myths and misconceptions: Artificial intelligence and cancer information.","volume":"7","author":"Johnson","year":"2023","journal-title":"JNCI Cancer Spectr."},{"key":"B36","doi-asserted-by":"publisher","first-page":"208","DOI":"10.3390\/ijms24010208","article-title":"Discogenic low back pain: Anatomy, pathophysiology and treatments of intervertebral disc degeneration.","volume":"24","author":"Mohd Isa","year":"2022","journal-title":"Int J Mol Sci."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1016\/j.mayocp.2015.10.009","article-title":"Evaluation and treatment of low back pain: A clinically focused review for primary care specialists.","volume":"90","author":"Hooten","year":"2015","journal-title":"Mayo Clin Proc."},{"key":"B38","doi-asserted-by":"publisher","first-page":"2226719","DOI":"10.1080\/24740527.2023.2226719","article-title":"Delivering team-based primary care for the management of chronic low back pain: An interpretive description qualitative study of healthcare provider perspectives.","volume":"7","author":"Vader","year":"2023","journal-title":"Can J Pain."},{"key":"B39","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1007\/s11606-019-05086-4","article-title":"VA\/DoD clinical practice guideline: Diagnosis and treatment of low back pain.","volume":"34","author":"Pangarkar","year":"2019","journal-title":"J Gen Intern Med."},{"key":"B40","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.3390\/medicina57111230","article-title":"Machine learning approaches to predict chronic lower back pain in people aged over 50 years.","volume":"57","author":"Shim","year":"2021","journal-title":"Medicina (Kaunas)."},{"key":"B41","doi-asserted-by":"publisher","first-page":"102530","DOI":"10.1016\/j.nicl.2020.102530","article-title":"Multi-modal biomarkers of low back pain: A machine learning approach.","volume":"29","author":"Lamichhane","year":"2021","journal-title":"Neuroimage Clin."},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.13375","article-title":"Capabilities of gpt-4 on medical challenge problems.","author":"Nori","year":"2023","journal-title":"arXiv [Preprint]"},{"key":"B43","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1007\/s00405-023-08104-8","article-title":"Exploring the potential of Chat-GPT as a supportive tool for sialendoscopy clinical decision making and patient information support.","volume":"281","author":"Chiesa-Estomba","year":"2024","journal-title":"Eur Arch Otorhinolaryngol."},{"key":"B44","doi-asserted-by":"publisher","first-page":"e48002","DOI":"10.2196\/48002","article-title":"Performance of GPT-3.5 and GPT-4 on the Japanese medical licensing examination: Comparison study.","volume":"9","author":"Takagi","year":"2023","journal-title":"JMIR Med Educ."},{"key":"B45","doi-asserted-by":"publisher","first-page":"e40822","DOI":"10.7759\/cureus.40822","article-title":"Artificial intelligence in ophthalmology: A comparative analysis of GPT-3.5, GPT-4, and human expertise in answering StatPearls questions.","volume":"15","author":"Moshirfar","year":"2023","journal-title":"Cureus."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1599241\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T05:36:37Z","timestamp":1747200997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1599241\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":45,"alternative-id":["10.3389\/fmed.2025.1599241"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1599241","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]},"article-number":"1599241"}}