{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T19:05:41Z","timestamp":1775329541708,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819549597","type":"print"},{"value":"9789819549603","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4960-3_32","type":"book-chapter","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T04:58:20Z","timestamp":1764133100000},"page":"396-408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0599-7620","authenticated-orcid":false,"given":"Thanh Binh","family":"Le","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7631-2450","authenticated-orcid":false,"given":"Hoang Nhat Khang","family":"Vo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4429-2343","authenticated-orcid":false,"given":"Tan Ha","family":"Mai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8044-8870","authenticated-orcid":false,"given":"Trong Nhan","family":"Phan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"issue":"9","key":"32_CR1","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta, J.N., Falcone, G.J., Rajpurkar, P., Topol, E.J.: Multimodal biomedical ai. Nat. Med. 28(9), 1773\u20131784 (2022)","journal-title":"Nat. Med."},{"issue":"8","key":"32_CR2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s10462-025-11185-y","volume":"58","author":"M Al-antari","year":"2025","unstructured":"Al-antari, M., et al.: Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artif. Intell. Rev. 58(8), 221 (2025)","journal-title":"Artif. Intell. Rev."},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019)","DOI":"10.18653\/v1\/W19-1909"},{"issue":"1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1002\/jbmr.3849","volume":"35","author":"JE Burns","year":"2020","unstructured":"Burns, J.E., Yao, J., Summers, R.M.: Artificial intelligence in musculoskeletal imaging: a paradigm shift. J. Bone Miner. Res. 35(1), 28\u201335 (2020)","journal-title":"J. Bone Miner. Res."},{"key":"32_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920, JMLR.org (2020)"},{"issue":"7","key":"32_CR6","doi-asserted-by":"publisher","first-page":"E128","DOI":"10.1097\/BRS.0000000000005265","volume":"50","author":"M Cheng","year":"2025","unstructured":"Cheng, M., et al.: Global, regional, and national burden of low back pain: findings from the global burden of disease study 2021 and projections to 2050. Spine 50(7), E128\u2013E139 (2025)","journal-title":"Spine"},{"issue":"10","key":"32_CR7","doi-asserted-by":"publisher","first-page":"5971","DOI":"10.3390\/ijerph19105971","volume":"19","author":"F D\u2019Antoni","year":"2022","unstructured":"D\u2019Antoni, F., et al.: Artificial intelligence and computer aided diagnosis in chronic low back pain: a systematic review. Int. J. Environ. Res. Public Health 19(10), 5971 (2022)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"32_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.103001","volume":"157","author":"Z Deng","year":"2024","unstructured":"Deng, Z., et al.: OphGLM: an ophthalmology large language-and-vision assistant. Artif. Intell. Med. 157, 103001 (2024)","journal-title":"Artif. Intell. Med."},{"issue":"11","key":"32_CR9","doi-asserted-by":"publisher","first-page":"838","DOI":"10.2519\/jospt.2011.3618","volume":"41","author":"TW Flynn","year":"2011","unstructured":"Flynn, T.W., Smith, B., Chou, R.: Appropriate use of diagnostic imaging in low back pain: a reminder that unnecessary imaging may do as much harm as good. J. Orthop. Sports Phys. Therapy 41(11), 838\u2013846 (2011)","journal-title":"J. Orthop. Sports Phys. Therapy"},{"key":"32_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110022","volume":"190","author":"C Hsieh","year":"2025","unstructured":"Hsieh, C., et al.: DALL-M: context-aware clinical data augmentation with large language models. Comput. Biol. Med. 190, 110022 (2025)","journal-title":"Comput. Biol. Med."},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Huang, S.C., Shen, L., Lungren, M.P., Yeung, S.: Gloria: a multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3942\u20133951 (2021)","DOI":"10.1109\/ICCV48922.2021.00391"},{"issue":"3","key":"32_CR12","first-page":"219","volume":"11","author":"Y.C Huang","year":"2024","unstructured":"Huang, Y..C., et al.: A comprehensive review on synergy of multi-modal data and AI technologies in medical diagnosis. Diagnostics 11(3), 219 (2024)","journal-title":"Diagnostics"},{"issue":"2","key":"32_CR13","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1177\/21925682241274372","volume":"15","author":"W Liawrungrueang","year":"2025","unstructured":"Liawrungrueang, W.: Others: artificial intelligence-assisted MRI diagnosis in lumbar degenerative disc disease: a systematic review. Global Spine Journal 15(2), 1405\u20131418 (2025)","journal-title":"Global Spine Journal"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"32_CR15","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 (2019)"},{"key":"32_CR16","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, vol.\u00a0139, pp. 8748\u20138763. PMLR (2021)"},{"key":"32_CR17","unstructured":"Sudirman, S., et al.: Lumbar spine MRI dataset. Mendeley Data, V2 (2019)"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Thawakar, O.C., et al.: XRayGPT: chest radiographs summarization using large medical vision-language models. In: Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pp. 440\u2013448. Association for Computational Linguistics, Bangkok, Thailand (2024)","DOI":"10.18653\/v1\/2024.bionlp-1.35"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, Y.: MedCLIP: contrastive learning from unpaired medical images and text. In: NeurIPS Workshops (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Wei, J., Zou, K.: EDA: Easy data augmentation techniques for boosting performance on text classification tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6382\u20136388. Association for Computational Linguistics, Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1670"},{"key":"32_CR21","unstructured":"Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. In: Proceedings of the 7th Machine Learning for Healthcare Conference, pp. 2\u201325. PMLR (2022)"},{"key":"32_CR22","unstructured":"Zhang, Y., Liu, H., Hu, J., Liu, J., Yang, J., Jin, Z.: Clip-driven universal model for organ segmentation and tumor detection. In: ICCV (2023)"},{"key":"32_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103551","volume":"102","author":"Z Zhao","year":"2025","unstructured":"Zhao, Z., et al.: CLIP in medical imaging: a survey. Med. Image Anal. 102, 103551 (2025)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Multi-disciplinary Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4960-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:03:09Z","timestamp":1764198189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4960-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"ISBN":["9789819549597","9789819549603"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4960-3_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]},"assertion":[{"value":"27 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miwai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miwai25.miwai.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}