Clinical decision support systems for the management of low back pain: A systematic review
Keywords:
low back pain, low back pain management, clinical decisional support system, systematic reviewAbstract
Background and aim: Low back pain (LBP) is one of the most prevalent musculoskeletal disorders and a major contributor to years lived with disability worldwide. Despite international guidelines promoting early conservative management and rational imaging use, clinical practice remains highly variable. Clinical Decision Support Systems (CDSS), particularly those integrating artificial intelligence (AI), have been developed to support diagnostic accuracy and standardize care. This review aims to synthesize current evidence on the performance, applications, and clinical integration of CDSS in the management of LBP.
Methods: A systematic review was conducted according to PRISMA 2020 guidelines. Searches were performed in PubMed, Scopus, ProQuest, and PsycINFO up to July 2025, including original studies evaluating CDSS for diagnosis or treatment planning in adults with LBP. Data extraction covered study design, CDSS type, data sources, performance metrics, and clinical outcomes. Risk of bias was assessed using QUADAS-2/QUADAS-AI, RoB 2, and ROBINS-I tools as appropriate. Descriptive statistics were computed for accuracy, sensitivity, specificity, and area under the curve (AUC).
Results: Nineteen studies met the inclusion criteria. Mean diagnostic accuracy was 0.911 (median 0.916), with corresponding mean sensitivity, specificity, and AUC of 0.865, 0.896, and 0.830, respectively. AI-based and hybrid systems performed comparably to rule-based models, while imaging optimization studies showed reductions of approximately 10% in unnecessary imaging and 15% in MRI utilization.
Conclusions: CDSS demonstrate high diagnostic performance and potential to improve guideline adherence and resource efficiency in LBP care. Broader implementation requires evaluation of long-term patient outcomes, cost-effectiveness, and real-world integration within electronic health records.
References
1. Ferreira ML, De Luca K, Haile LM, et al. Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(6):e316–e329. doi:10.1016/S2665-9913(23)00098-X
2. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
3. Dijk SW, Wollny C, Barkhausen J, et al. Evaluation of a clinical decision support system for imaging requests: a cluster randomized clinical trial. JAMA. 2025;333:7439. doi:10.1001/jama.2024.27853
4. Tsega S, Krouss M, Alaiev D, et al. Imaging wisely campaign: initiative to reduce imaging for low back pain across a large safety net system. J Am Coll Radiol. 2024;21(1):165–174. doi:10.1016/j.jacr.2023.07.012
5. Chen R, Zhang P, Mao Z, et al. Artificial intelligence for lumbosacral degenerative spine disease: advancements, challenges, and future directions. World Neurosurg. 2020;134:e672–e682. doi:10.1016/j.wneu.2019.11.031
6. Preti LM, Ardito V, Compagni A, Petracca F, Cappellaro G. Implementation of machine learning applications in health care organizations: systematic review of empirical studies. J Med Internet Res. 2024;26:e55897. doi:10.2196/55897
7. Shokri P, Zahmatyar M, Falah Tafti M, et al. Non-spinal low back pain: global epidemiology, trends, and risk factors. Health Sci Rep. 2023;6(9):e1533. doi:10.1002/hsr2.1533
8. World Health Organization. Low back pain – fact sheet. Geneva: WHO; 2023. INCOMPLETE (website fields missing)
9. Fatoye F, Gebrye T, Mbada CE, Useh U. Clinical and economic burden of low back pain in low- and middle-income countries: a systematic review. BMJ Open. 2023;13(4):e064119. doi:10.1136/bmjopen-2022-064119
10. Gushcha AO, Sharif S, Zileli M, Oertel J, Zygourakis CC, Yusupova AR. Acute back pain: clinical and radiologic diagnosis: WFNS spine committee recommendations. World Neurosurg X. 2024;22:100278. doi:10.1016/j.wnsx.2024.100278
11. Dunn M, Rushton AB, Mistry J, Soundy A, Heneghan NR. The biopsychosocial factors associated with development of chronic musculoskeletal pain: an umbrella review and meta-analysis of observational systematic reviews. PLoS One. 2024;19(4):e0294830. doi:10.1371/journal.pone.0294830
12. Jahn A, Andersen JH, Christiansen DH, Seidler A, Dalbøge A. Occupational mechanical exposures as risk factor for chronic low-back pain: a systematic review and meta-analysis. Scand J Work Environ Health. 2023;49(7):453–465. doi:10.5271/sjweh.4114
13. World Health Organization. (2023). WHO guideline for non-surgical management of chronic primary low back pain. Geneva: WHO. Organizzazione Mondiale della Sanità. Available from: https://www.who.int/publications/i/item/9789240081789
14. Low back pain and sciatica in over 16s: assessment and management. London: National Institute for Health and Care Excellence (NICE); 2020 Dec 11. (NICE Guideline, No. 59.) Available from: https://www.ncbi.nlm.nih.gov/books/NBK562933/
15. Nicol V, Verdaguer C, Daste C, et al. Chronic low back pain: a narrative review of recent international guidelines for diagnosis and conservative treatment. J Clin Med. 2023;12(4):1685. doi:10.3390/jcm12041685
16. Hall AM, Kamper SJ, Maher CG. Do not routinely offer imaging for uncomplicated low back pain. BMJ. 2021;372:n291. doi:10.1136/bmj.n291
17. Samanna CL, Buntine P, Belavy DL, et al. Adherence to low back pain clinical guidelines in Australian hospital emergency departments: a public and private comparison. Australas Emerg Care. 2024;27(4):276–281. doi:10.1016/j.auec.2024.07.001
18. Heine J, Window P, Hacker S, et al. Adherence to recommended guidelines for low back pain presentations to an Australian emergency department: barriers and enablers. Australas Emerg Care. 2023;26(4):326–332. doi:10.1016/j.auec.2023.04.003
19. Toh ZA, Polat A, Liew D. Clinical decision support systems in spine care: a scoping review. JMIR Med Inform. 2024;12:e58122. doi:10.2196/58122
20. Tagliaferri SD, Angelova M, Zhao X, et al. Artificial intelligence to improve back pain outcomes: machine learning and beyond. NPJ Digit Med. 2020;3:93. doi:10.1038/s41746-020-0289-6
21. Kierner S, Kierner P, Kucharski J. Combining machine learning models and rule engines in clinical decision systems: exploring optimal aggregation methods for vaccine hesitancy prediction. Comput Biol Med. 2025;188:109749. doi:10.1016/j.compbiomed.2025.109749
22. Giaccone M, Ferraro S, Placci A, Giorgi Pierfranceschi M. AI-based decision support systems in lumbar degenerative spine disorders: a systematic review. BMC Musculoskelet Disord. 2025;26:85. doi:10.1186/s12891-025-08254-1
23. Rudin S, Sharma A, Agrawal A. A patient-centered clinical decision support tool for low back pain: usability study. JMIR Form Res. 2025;9:e58051. doi:10.2196/58051
24. Chen D, Bhambhvani HP, Hom J, et al. Effect of electronic clinical decision support on imaging for the evaluation of acute low back pain in the ambulatory care setting. World Neurosurg. 2020;134:e874–e877. INCOMPLETE (missing DOI)
25. Zafar HM, Ip IK, Mills AM, Raja AS, Langlotz CP, Khorasani R. Effect of clinical decision support-generated report cards versus real-time alerts on primary care provider guideline adherence for low back pain outpatient lumbar spine MRI orders. AJR Am J Roentgenol. 2019;212(2):386–394. doi:10.2214/AJR.18.19780
26. Benditz A, Faber F, Wenk G, et al. The role of a decision support system in back pain diagnoses: a pilot study. Biomed Res Int. 2019;2019:1314028. doi:10.1155/2019/1314028
27. Benditz A, Pulido LC, Grifka J, Ripke F, Jansen P. A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study. Arch Orthop Trauma Surg. 2023;143(2):621–625. doi:10.1007/s00402-021-04080-y
28. Maheronnaghsh R, Nezareh S, Sayyah MK, Rahimi-Movaghar V. Developing SNOMED-CT for decision making and data gathering: a software prototype for low back pain. Acta Med Iran. 2013;51(8):548–553.
29. Fakharian E, Nabovati E, Farzandipour M, Akbari H, Saeedi S. Diagnosis of mechanical low back pain using a fuzzy logic-based approach. Int J Intell Syst Appl Eng. 2021;9(3):116–120. doi:10.18201/ijisae.2021.239
30. Kobayashi H, Sekiguchi M, Yonemoto K, et al; DISTO project working group. Reference values of the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire in patients with lumbar spinal stenosis and characteristics of deterioration of quality of life: lumbar spinal stenosis diagnosis support tool (DISTO project). J Orthop Sci. 2019;24(4):584–589. doi:10.1016/j.jos.2018.11.022
31. Al-Kasasbeh R, Korenevskiy N, Ionescu F, Alshamasin M, Smith AP, Alwadie A. Biotechnical measurement and software system for the prediction and diagnosis of osteochondrosis of the lumbar region based on acupuncture points with the use of fuzzy logic rules. Biomed Tech. 2013;58(1):51–65. doi:10.1515/bmt-2012-0081
32. Tun Firzara AM, Teo CH, Teh SY, et al. Evaluation of an electronic clinical decision support system (DeSSBack) to improve low back pain management: a pilot cluster randomized controlled trial. Fam Pract. 2023;40(5–6):742–752. doi:10.1093/fampra/cmad044
33. Gal N, Stoicu-Tivadar V, Andrei D, Nemeș DI, Nădășan E. Computer assisted treatment prediction of low back pain pathologies. In: Stoicu-Tivadar L, et al., editors. Cross-border challenges in informatics with a focus on disease surveillance and utilising big-data. Amsterdam: IOS Press; 2014. p.47–51. doi:10.3233/978-1-61499-389-6-4
34. Oude Nijeweme-d’Hollosy W, van Velsen L, Poel M, Groothuis-Oudshoorn CGM, Soer R, Hermens H. Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int J Med Inform. 2018;110:31–41. doi:10.1016/j.ijmedinf.2017.11.010
35. Altun S, Alkan A. Lumbar spinal stenosis analysis with deep learning based decision support systems. GU J Sci. 2023;36(3):1200–1215. doi:10.35378/gujs.1116423
36. Gambo I, Mbada C, Aina S, et al. Implementing a decision support tool for low-back pain diagnosis and prediction based on the range of motions. Indones J Electr Eng Comput Sci. 2024;33(2):1302–1314. doi:10.11591/ijeecs.v33.i2.pp1302-1314
37. Zhang D, Du J, Shi J, et al. A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning. JOR Spine. 2024;7(2):e1342. doi:10.1002/jsp2.1342
38. Øverås CK, Nilsen TIL, Nicholl BI, et al. Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability. BMC Med. 2022;20:53. doi:10.1186/s12916-022-02237-z
39. Sandal LF, Øverås CK, Nordstoga AL, et al. A digital decision support system (SELFBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up. Pilot Feasibility Stud. 2020;6:72. doi:10.1186/s40814-020-00604-2
40. Lin L, Hu PJH, Sheng ORL. A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations. Decis Support Syst. 2006;42(2):1152–1169. doi:10.1016/j.dss.2005.10.007
41. Kadhim MA. FNDSB: a fuzzy-neuro decision support system for back pain diagnosis. Cogn Syst Res. 2018;52:691–700. doi:10.1016/j.cogsys.2018.08.021
42. Badahman F, Alsobhi M, Alzahrani A, et al. Validating the accuracy of a patient-facing clinical decision support system in predicting lumbar disc herniation: diagnostic accuracy study. Diagnostics. 2024;14(17):1870. doi:10.3390/diagnostics14171870
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Copyright (c) 2025 Elisa Caria, Giulia Delrio, Ilaria Pinna, Salvatore Sardu, Corrado Ciatti, Francesco Muresu, Fabio Milia, Gianfilippo Caggiari, Carlo Doria
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