KaydedilenlerDerginizi ekleyin

Dergiler / Acta Infologica / 2026 / Volume: 10 Issue: 1

Predicting Low Back Pain Using Machine Learning Techniques: A Systematic Review

Demet Köseoğlu(KARAMANOGLU MEHMETBEY UNIVERSITY)0000-0002-9153-9276
Mehmet Cabir Akkoyunlu(KARAMANOGLU MEHMETBEY UNIVERSITY)0000-0002-9388-6554
Sayfa
—
Yayın Tarihi
2026-06-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Advancing these dimensions is critical for translating ML–based LBP models from experimental settings into routine clinical decision support systems and for shifting healthcare delivery from reactive management to proactive, data-driven care ecosystems.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Low back pain (LBP) is one of the leading causes of disability worldwide, posing substantial clinical and socioeconomic challenges.
📊
Temel Bulgular & Veri:This systematic review aims to synthesize current evidence on the application of ML techniques in the diagnosis and clinical management of LBP and to identify methodological patterns and performance trends.
💡
Sonuç & Katkı:Advancing these dimensions is critical for translating ML–based LBP models from experimental settings into routine clinical decision support systems and for shifting healthcare delivery from reactive management to proactive, data-driven care ecosystems.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2017 Verileri
⚙️Değişkenler:Healthcare · Machine Learning · Health Informatics

Abstract

Low back pain (LBP) is one of the leading causes of disability worldwide, posing substantial clinical and socioeconomic challenges. Machine learning (ML) has introduced new opportunities for improving early diagnosis, risk stratification, and treatment guidance in LBP management. This systematic review aims to synthesize current evidence on the application of ML techniques in the diagnosis and clinical management of LBP and to identify methodological patterns and performance trends. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 27 peer-reviewed studies published between 2017 and 2023 were systematically identified and evaluated according to predefined inclusion and exclusion criteria. The findings indicate that Convolutional Neural Networks (CNNs) achieve high accuracy rates (98%–99%) in large-scale and imaging-based datasets, such as magnetic resonance imaging (MRI) and EHR data. SVMs demonstrate strong predictive performance in real-time sensor signal analysis, while ensemble methods, including Random Forest and XGBoost, yield robust results in structured clinical and biomarker datasets. Nevertheless, substantial methodological barriers remain. These include data heterogeneity and noise in PROM–based datasets, performance decline during external validation in real-world clinical settings, and limited interpretability due to the black-box nature of many ML models. This review highlights the need for multimodal data fusion strategies and the integration of Explainable Artificial Intelligence (XAI) frameworks to enhance model transparency, generalizability, and clinical trust. Advancing these dimensions is critical for translating ML–based LBP models from experimental settings into routine clinical decision support systems and for shifting healthcare delivery from reactive management to proactive, data-driven care ecosystems.

🔖

Bu Makaleye Atıf Yap

Köseoğlu, D., Akkoyunlu, M. C. (2026). Predicting Low Back Pain Using Machine Learning Techniques: A Systematic Review. Acta Infologica, Volume: 10 Issue: 1. https://doi.org/10.26650/acin.1704458

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (64)

  1. Abdollahi, M., Ashouri, S., Abedi, M., Azadeh-Fard, N., Parnianpour, M., Khalaf, K., & Rashedi, E. (2020). Using a motion sensor to categorize nonspecific low back pain patients: A machine learning approach. Sensors (Switzerland), 20(12), 1–16. https://doi.org/10.3390/s20123600 google scholar
  2. Abdullah, A. A., Yaakob, A. & Ibrahim, Z. (2018). Prediction of spinal abnormalities using machine learning techniques. ICASSDA 2018, 1–6. https://doi.org/10.1109/ICASSDA.2018.8477622 google scholar
  3. Adeoye, J., Tan, J. Y., Choi, S. W., & Thomson, P. (2021). Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. International Journal of Medical Informatics, 154(August). https://doi.org/10.1016/j.ijmedinf.2021.104557 google scholar
  4. Al-antari, M. A., Salem, S., Raza, M., Elbadawy, A. S., Bütün, E., Aydin, A. A., Aydoğan, M., Ertuğrul, B., Talo, M., & Gu, Y. H. (2025). Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artificial Intelligence Review, 58(221), 1–78. https://doi.org/10.1007/s10462-025-11185-y google scholar
  5. Altınay Mimaroğlu, B. (2022). Bel ve Boyun Ağrısı Yaşayan Hastalarda Teşhis ve Tedavi Seçimi İçin MR Görüntüsü Olmadan Karar Modeli. Department of Health Informatics, Middle East Technical University google scholar
  6. Amorim, P., Paulo, J. R., Silva, P. A., Peixoto, P., Castelo-Branco, M., & Martins, H. (2021). Machine learning applied to low back pain rehabilitation–a systematic review. International Journal of Digital Health, 1(1), 10. https://doi.org/10.29337/ijdh.34 google scholar
  7. Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022). A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Medical Engineering and Physics, 105(February). https://doi.org/10.1016/j.medengphy.2022.103825 google scholar
  8. Bianchi, J., de Oliveira Ruellas, A. C., Gonçalves, J. R., Paniagua, B., Prieto, J. C., Styner, M., Li, T., Zhu, H., Sugai, J., Giannobile, W., Benavides, E., Soki, F., Yatabe, M., Ashman, L., Walker, D., Soroushmehr, R., Najarian, K., & Cevidanes, L. H. S. (2020). Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning. Scientific Reports, 10(1), 1–14. https://doi.org/10.1038/s41598-020-64942-0 google scholar
  9. Bianco, A., Al-Azzawi, Z. A. M., Guadagno, E., Osmanlliu, E., Gravel, J., & Poenaru, D. (2023). Use of machine learning in pediatric surgical clinical prediction tools: A systematic review. Journal of Pediatric Surgery, 58(5), 908–916. https://doi.org/10.1016/j.jpedsurg.2023.01.020 google scholar
  10. Campagner, A., Berjano, P., Lamartina, C., Langella, F., Lombardi, G., & Cabitza, F. (2020). Assessment and prediction of spine surgery invasiveness with machine learning techniques. Computers in Biology and Medicine, 121(May), 103796. https://doi.org/10.1016/j.compbiomed.2020.103796 google scholar
  11. Chen, R., Zhang, S., Zheng, Y., Yu, Q., & Wang, C. (2025). Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology. Frontiers in Medicine, 12:1599241, 1–9. https://doi.org/10.3389/fmed.2025.1599241 google scholar
  12. Chu, Q., Wang, X., Lv, H., Zhou, Y., & Jiang, T. (2025). Vision transformer ‑ based diagnosis of lumbar disc herniation with grad ‑ CAM interpretability in CT imaging. BMC Musculoskeletal Disorders, 26(419), 1–13. https://doi.org/10.1186/s12891-025-08602-2 google scholar
  13. Chung, J. W. Y., Zeng, Y., & Wong, T. K. S. (2013). Drug therapy for the treatment of chronic nonspecific low back pain: systematic review and meta-analysis. Pain Physician, 16(6), 685–704. https://doi.org/10.36076/ppj.2013/16/e685 google scholar
  14. Cruz, A. D., Santhosini, P., Santhini, P., & Shirly, S. (2022). Comparative study and detection of spinal deformities using supervised machine learning algorithms. Proceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022, 1–6. https://doi.org/10.1109/IC3SIS54991.2022.9885287 google scholar
  15. D’antoni, F., Russo, F., Ambrosio, L., Bacco, L., Vollero, L., Vadalà, G., Merone, M., Papalia, R., & Denaro, V. (2022). Artificial intelligence and computer aided diagnosis in chronic low back pain: a systematic review. International Journal of Environmental Research and Public Health, 19(10). https://doi.org/10.3390/ijerph19105971 google scholar
  16. Datta, S., Hasan, S. M., Mitu, M., Taraque, M. F., Jannat, N., & Efat, A. H. (2023). Hyperparameter-tuned machine learning models for complex medical datasets classification. 3rd International Conference on Electrical, Computer and Communication Engineering, ECCE 2023, 1–6. https://doi.org/10.1109/ECCE57851.2023.10101525 google scholar
  17. Donisi, L., Coccia, A., Amitrano, F., Mercogliano, L., Cesarelli, G., & D’Addio, G. (2020). Backpack influence on kinematic parameters related to timed up and go (TUG) test in school children. IEEE Medical Measurements and Applications, MeMeA 2020 - Conference Proceedings, 1–5. https://doi.org/10.1109/MeMeA49120.2020.9137198 google scholar
  18. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z google scholar
  19. Ferlito, R., Blatti, C., Lucenti, L., Boscarino, U., Sapienza, M., Pavone, V., & Testa, G. (2022). Pain education in the management of patients with chronic low back pain: a systematic review. Journal of Functional Morphology and Kinesiology, 7(4). https://doi.org/10.3390/jfmk7040074 google scholar
  20. Fritz, J. M., Childs, J. D., Wainner, R. S., & Flynn, T. W. (2012). Primary care referral of patients with low back pain to physical therapy: Impact on future healthcare utilization and costs. Spine, 37(25), 2114–2121. https://doi.org/10.1097/BRS.0b013e31825d32f5 google scholar
  21. Gencdogmus, A., Keskin, S. R., Dogan, G., & Ozturk, Y. (2019). A data-driven approach to kinematic analytics of spinal motion. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, 2222–2229. https://doi.org/10.1109/BigData47090.2019.9006164 google scholar
  22. Gross, D. P., Armijo-Olivo, S., Shaw, W. S., Williams-Whitt, K., Shaw, N. T., Hartvigsen, J., Qin, Z., Ha, C., Woodhouse, L. J., & Steenstra, I. A. (2016). Clinical decision support tools for selecting interventions for patients with disabling musculoskeletal disorders: A scoping review. Journal of Occupational Rehabilitation, 26(3), 286–318. https://doi.org/10.1007/s10926-015-9614-1 google scholar
  23. Guleria, K., Sharma, S., Kumar, S., & Tiwari, S. (2022). Early prediction of hypothyroidism and multiclass classification using predictive machine learning and deep learning. Measurement: Sensors, 24(September). https://doi.org/10.1016/j.measen.2022.100482 google scholar
  24. Hager, P., Jungmann, F., Holland, R., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Makowski, M., Braren, R., Kaissis, G. & Rueckert, D. (2024). Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nature medicine, 30(9), 2613-2622. https://doi.org/10.1038/s41591-024-03097-1 google scholar
  25. Haider, F., Albert, P., & Luz, S. (2020). Automatic recognition of low-back chronic pain level and protective movement behavior using physical and muscle activity information. Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020, 834–838. https://doi.org/10.1109/FG47880.2020.00065 google scholar
  26. Hartvigsen, J., Hancock, M. J., Kongsted, A., Louw, Q., Ferreira, M. L., Genevay, S., Hoy, D., Karppinen, J., Pransky, G., Sieper, J., Smeets, R. J., Underwood, M., Buchbinder, R., Cherkin, D., Foster, N. E., Maher, C. G., van Tulder, M., Anema, J. R., Chou, R., … Woolf, A. (2018). What low back pain is and why we need to pay attention. Lancet, 391(10137), 2356–2367. https://doi.org/10.1016/S0140-6736(18)30480-X google scholar
  27. Hasan, K. A., & Hasan, M. A. M. (2020). Identification and analysis of risk factors of lower back pain using machine learning approaches. Proceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020, 129–132. https://doi.org/10.1109/ICECE51571.2020.9393098 google scholar
  28. Huang, Z. Y., Guo, W., & Martin, J. T. (2023). Socioeconomic status, mental health, and nutrition are the principal traits for low back pain phenotyping: Data from the osteoarthritis initiative. Journal of orthopedic Spine, 6(2), 1–12. https://doi.org/10.1002/jsp2.1248 google scholar
  29. Hutton, B., Catalá-López, F., & Moher, D. (2016). The PRISMA statement extension for systematic reviews incorporating network meta-analysis: PRISMA-NMA. Medicina Clínica (English Edition), 147(6), 262–266. https://doi.org/10.1016/j.medcle.2016.10.003 google scholar
  30. Islam, M. S., Asaduzzaman, M., & Rahman, M. M. (2019). Feature selection and classification of spinal abnormalities to detect low back pain disorder using machine learning approaches. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019, 2019(Icasert), 1–4. https://doi.org/10.1109/ICASERT.2019.8934683 google scholar
  31. Jenssen, M. D. K., Bakkevoll, P. A., Ngo, P. D., Budrionis, A., Fagerlund, A. J., Tayefi, M., Bellika, J. G., & Godtliebsen, F. (2021). Machine learning in chronic pain research: A scoping review. Applied Sciences (Switzerland), 11(7), 1–18. https://doi.org/10.3390/app11073205 google scholar
  32. Jiang, N., Luk, K. D.-K., & Hu, Y. (2017). A machine learning based surface electromyography topography evaluation for prognostic prediction of functional restoration rehabilitation in chronic low back pain. Wolters Kluwer Health – SPINE, 852, 159–161. https://doi.org/10.1097/BRS.0000000000002159 google scholar
  33. Kocaman, H., Yıldırım, H., Gökşen, A., & Arman, G. M. (2023). An investigation of machine learning algorithms for prediction of lumbar disc herniation. Medical and Biological Engineering and Computing, 2785–2795. https://doi.org/10.1007/s11517-023-02888-x google scholar
  34. Lamichhane, B., Jayasekera, D., Jakes, R., Glasser, M. F., Zhang, J., Yang, C., Grimes, D., Frank, T. L., Ray, W. Z., Leuthardt, E. C., & Hawasli, A. H. (2021). Multi-modal biomarkers of low back pain: A machine learning approach. NeuroImage: Clinical, 29, 102530. https://doi.org/10.1016/j.nicl.2020.102530 google scholar
  35. Miotto R, Percha B. L, Glicksberg B. S, Lee H. C, Cruz L, Dudley, J. T., & Nabeel, I. (2020). Identifying acute low back pain episodes in primary care practice from clinical notes: Observational study. JMIR Medical Informatics, 8(2). https://doi.org/10.2196/16878 google scholar
  36. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/j.ijsu.2010.02.007 google scholar
  37. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. (2015). Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Systematic Reviews, 4(1), 1–9. https://doi.org/doi.org/10.1186/2046-4053-4-1 google scholar
  38. Mork, P. J., & Bach, K. (2018). A decision support system to enhance self-management of low back pain: Protocol for the selfBACK project. Journal of Management Information Research Protocols, 7(7), 1–12. https://doi.org/10.2196/resprot.9379 google scholar
  39. Nabiyev, V., Ayhan, S. & Acaroğlu, E. (2015). Bel ağrısında tanı ve tedavi algoritması. TOTBID Dergisi, 14(4), 242-251. https://doi.org/10.14292/totbid.dergisi.2015.40 google scholar
  40. Nasir, U. I. Z., Khan, K., & Asghar, M. (2023). Diagnosing spinal abnormalities using machine learning: A data-driven approach. 3rd IEEE International Conference on Artificial Intelligence, ICAI 2023, February, 20–25. https://doi.org/10.1109/ICAI58407.2023.10136651 google scholar
  41. Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A. & Rashidi, P. (2024). Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine, 154, 102900. https://doi.org/10.1016/j.artmed.2024.102900 google scholar
  42. Nordin, N., Zainol, Z., Mohd Noor, M. H., & Chan, L. F. (2022). Suicidal behavior prediction models using machine learning techniques: A systematic review. Artificial Intelligence in Medicine, 132(January). https://doi.org/10.1016/j.artmed.2022.102395 google scholar
  43. Oguzoncul, A. F. (2021). Halk Sağlığı Bakışıyla Türkiye’de Kronik Hastalıklar. October 2020. google scholar
  44. Oude Nijeweme-d’Hollosy, W., van Velsen, L., Poel, M., Groothuis-Oudshoorn, C. G. M., Soer, R., & Hermens, H. (2018). Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. International Journal of Medical Informatics, 110(August 2017), 31–41. https://doi.org/10.1016/j.ijmedinf.2017.11.010 google scholar
  45. Paramesti, M. A., Prawiningrum, A. F., Syababa, A. D. H., Munggaran, H. R., Harimurti, S., Adiprawita, W., Anshori, I., & Herman, I. (2019). Lower back pain classification using machine learning. 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering, APCoRISE 2019. https://doi.org/10.1109/APCoRISE46197.2019.9318818 google scholar
  46. Salehi, E., Yousefi, H., Rashidi, H., & Ghanaatti, H. (2019). Automatic diagnosis of disc herniation in two-dimensional MR images with combination of distinct features using machine learning methods. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019, 1–6. https://doi.org/10.1109/EBBT.2019.8742052. google scholar
  47. Samah, H., Karlob, N., Jessicaa, B., Andrea, D. F., Dineshb, K., & Lisa, C. (2023). Identifying chronic low back pain phenotypic domains and characteristics accounting for individual variation: a systematic review. Pain, 164(10), 2148–2190. https://doi.org/10.1097/j.pain.0000000000002911 google scholar
  48. Sandag, G. A., Tedry, N. E., & Lolong, S. (2019). Classification of lower back pain using k-nearest neighbor algorithm. Citsm, 6th International Conference on Cyber and IT Service Management, CITSM 2018, 1–5. https://doi.org/10.1109/CITSM.2018.8674361 google scholar
  49. Santos-Moreno, P., Sucerquia-Quintero, J. A., & García-Salinas, R. (2022). Chronic low back pain: diagnostic approach for primary care. Revista Colombiana de Reumatología (English Edition), 29(4), 303–309. https://doi.org/10.1016/j.rcreue.2021.02.006 google scholar
  50. Schwartz, L., Anteby, R., Klang, E., & Soffer, S. (2023). Stroke mortality prediction using machine learning: systematic review. Journal of the Neurological Sciences, 444(July 2022). https://doi.org/10.1016/j.jns.2022.120529 google scholar
  51. Şevgin, Ö., Buğday, B., & Ekinci, M. (2023). Kronik bel ağrısında ağrı inançlarının cinsiyetlere göre semptom şiddeti ile ilişkisi. Anatolian Journal of Health Research, 4(2), 66–71. https://doi.org/10.29228/anatoljhr.71040 google scholar
  52. Soin, A., Hirschbeck, M., Verdon, M., & Manchikanti, L. (2022). A pilot study implementing a machine learning algorithm to use artificial intelligence to diagnose spinal conditions. Pain Physician, 25(2), 171–178. https://doi.org/10.1016/j.painp.2022.02.017. google scholar
  53. Sustersic, T., Rankovic, V., Kovacevic, V., Milovanovic, V., Rasulic, L., & Filipovic, N. (2021). Machine Learning-based Image Processing in Support of Discus Hernia Diagnosis. BIBE 2021 – 21st IEEE International Conference on BioInformatics and BioEngineering, Proceedings. https://doi.org/10.1109/BIBE52308.2021.9635305 google scholar
  54. Tong, M. W., Ziegeler, K., Kreutzinger, V., & Majumdar, S. (2025). Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non- specific chronic low back pain. nature - Scientific Reports, 15:30690, 1–15. https://doi.org/10.1038/s41598-025-13619-7 google scholar
  55. Tsai, J. Y., Hung, I. Y. J., Guo, Y. L., Jan, Y. K., Lin, C. Y., Shih, T. T. F., Chen, B. Bin, & Lung, C. W. (2021). Lumbar disc herniation automatic detection in magnetic resonance imaging based on deep learning. Frontiers in Bioengineering and Biotechnology, 9, 1–11. https://doi.org/10.3389/fbioe.2021.708137 google scholar
  56. Usman, T. M., Saheed, Y. K., Nsang, A., Ajibesin, A., & Rakshit, S. (2023). A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression. Artificial Intelligence in Medicine, 143(June). https://doi.org/10.1016/j.artmed.2023.102617 google scholar
  57. Vassilaki, M., & Hurwitz, E. L. (2014). Insights in public health: perspectives on pain in the low back and neck: global burden, epidemiology, and management. Hawai’i journal of medicine & public health: a journal of Asia Pacific Medicine & Public Health, 73(4), 122–126. google scholar
  58. Verma, D., Bach, K., & Mork, P. J. (2021). Application of machine learning methods on patient reported outcome measurements for predicting outcomes: a literature review. Informatics, 8(3). https://doi.org/10.3390/informatics8030056 google scholar
  59. Verma, D., Bach, K., & Mork, P. J. (2023). External validation of prediction models for patient-reported outcome measurements collected using the SELFBACK mobile app. International Journal of Medical Informatics, 170(777090), 104936. https://doi.org/10.1016/j.ijmedinf.2022.104936 google scholar
  60. Verma, D., Jansen, D., Bach, K., Poel, M., Mork, P. J., & d’Hollosy, W. O. N. (2022). Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes. BMC Medical Informatics and Decision Making, 22(1), 1–11. https://doi.org/10.1186/s12911-022-01973-9 google scholar
  61. Wang, N, Xiao, J., & Cui, L. (2018). EasiSMR: Recognizing non-specific low back pain symptomatic muscles using multi-muscles fusion based machine learning. 2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018, 2002–2006. https://doi.org/10.1109/CompComm.2018.8780772 google scholar
  62. Wong, JJ., Côté, P., Sutton, DA., Randhawa, K., Yu, H., Varatharajan, S., Goldgrub, R., Nordin, M., Gross, DP., Shearer, HM., Carroll, LJ., Stern, PJ., Ameis, A., Southerst, D., Mior, S., Stupar, M., Varatharajan, T., & Taylor-Vaisey, A. (2017). Clinical practice guidelines for the noninvasive management of low back pain: A systematic review by the Ontario Protocol for Traffic Injury Management (OPTIMa) Collaboration. European Journal of Pain (United Kingdom), 21(2), 201–216. https://doi.org/10.1002/ejp.931 google scholar
  63. Al-antari, M. A., Salem, S., Raza, M., Elbadawy, A. S., Bütün, E., Aydin, A. A., Aydoğan, M., Ertuğrul, B., Talo, M., & Gu, Y. H. (2025). Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artificial Intelligence Review, 58(221), 1–78. https://doi.org/10.1007/s10462-025-11185-y google scholar
  64. Chen, R., Zhang, S., Zheng, Y., Yu, Q., & Wang, C. (2025). Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology. Frontiers in Medicine, 12:1599241, 1–9. https://doi.org/10.3389/fmed.2025.1599241 google scholar