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Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain

Document Type : Review Article

Authors

1 Research Center for Neuromodulation and Pain, NAB Pain Clinic, Shiraz University of Medical Sciences, Shiraz, Iran

2 Medis Holding, Shiraz, Iran

10.31661/jbpe.v0i0.2306-1629

Abstract

Chronic pain is a prevalent condition and the leading cause of work absenteeism worldwide. This condition involves persistent pain lasting more than three months, significantly impacting the quality of life and social interactions of patients. While the causes of chronic pain can often remain unknown, no definitive cure exists for the various known causes. Furthermore, the evaluation and prediction of pain can be challenging, particularly in unconscious patients receiving care in the intensive care unit. Subjective measures and traditional methods are typically employed for diagnosis, assessment, and treatment to identify the most effective approach. However, recent advancements in Artificial Intelligence (AI) and other computer science fields have revolutionized the medical domain, offering a novel and promising avenue for enhancing pain management. This review provides an overview of the potential benefits, limitations, and prospects associated with the role of AI in the diagnosis, assessment, and management of chronic pain.

Highlights

Aliasghar Karimi (Google Scholar)

Keywords

References
  1. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. 2019;7:e7702. doi: 10.7717/peerj.7702. PubMed PMID: 31592346. PubMed PMCID: PMC6779111.
  2. Bhattad PB, Jain V. Artificial intelligence in modern medicine - The evolving necessity of the present and role in transforming the future of medical care. 2020;12(5):e8041. doi: 10.7759/cureus.8041. PubMed PMID: 32528777. PubMed PMCID: PMC7282357.
  3. Karimi A, HaddadPajouh H. Artificial intelligence, important assistant of scientists and physicians. Galen Med J. 2020;9:e2048. doi: 10.31661/gmj.v9i0.2048. PubMed PMID: 34466625. PubMed PMCID: PMC8343766.
  4. Hadi MA, McHugh GA, Closs SJ. Impact of chronic pain on patients’ quality of life: A comparative mixed-methods study. J Patient Exp. 2019;6(2):133-41. doi: 10.1177/2374373518786013. PubMed PMID: 31218259. PubMed PMCID: PMC6558939.
  5. Yazdani A, Soroushfard Z, Nasiri A, Akbarimehr H, Bagheri JS, Fathidoulabi Z. Musculoskeletal pain and quality of life among office workers based on pain management and exercise therapy: A recommendation. Journal of Pain Management. 2022;15(3):243-6.
  6. Connor L, Dean J, McNett M, Tydings DM, Shrout A, Gorsuch PF, et al. Evidence-based practice improves patient outcomes and healthcare system return on investment: Findings from a scoping review. Worldviews Evid Based Nurs. 2023;20(1):6-15. doi: 10.1111/wvn.12621. PubMed PMID: 36751881.
  7. Lotsch J, Ultsch A, Mayer B, Kringel D. Artificial intelligence and machine learning in pain research: a data scientometric analysis. Pain Rep. 2022;7(6):e1044. doi: 10.1097/PR9.0000000000001044. PubMed PMID: 36348668. PubMed PMCID: PMC9635040.
  8. Matsangidou M, Liampas A, Pittara M, Pattichi CS, Zis P. Machine learning in pain medicine: An up-to-date systematic review. Pain Ther. 2021;10(2):1067-84. doi: 10.1007/s40122-021-00324-2. PubMed PMID: 34568998. PubMed PMCID: PMC8586126.
  9. Manne R, Kantheti SC. Application of artificial intelligence in healthcare: chances and challenges. Curr J Appl Sci Technol. 2021;40(6):78-89.
  10. Gutierrez L, Lim JS, Foo LL, Ng WY, Yip M, Lim GYS, et al. Application of artificial intelligence in cataract management: current and future directions. Eye Vis (Lond). 2022;9(1):3. doi: 10.1186/s40662-021-00273-z. PubMed PMID: 34996524. PubMed PMCID: PMC8739505.
  11. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459-86 doi: 10.1007/s12652-021-03612-z. PubMed PMID: 35039756. PubMed PMCID: PMC8754556.
  12. Kunze KN, Krivicich LM, Clapp IM, Bodendorfer BM, Nwachukwu BU, Chahla J, et al. Machine learning algorithms predict achievement of clinically significant outcomes after orthopaedic surgery: A systematic review. Arthroscopy. 2022;38(6):2090-105. doi: 10.1016/j.arthro.2021.12.030. PubMed PMID: 34968653.
  13. Zhang M, Zhu L, Lin SY, Herr K, Chi CL, Demir I, et al. Using artificial intelligence to improve pain assessment and pain management: a scoping review. J Am Med Inform Assoc. 2023;30(3):570-87. doi: 10.1093/jamia/ocac231. PubMed PMID: 36458955. PubMed PMCID: PMC9933069.
  14. Farjam M, Askari A, Hoseinipour A, Homayounfar R, Jamshidi J, Khodabakhshi F, et al. A cohort study protocol of low back pain in rural area inhabitants: Fasa Low Back Pain Cohort Study (FABPACS). Galen Med J. 2016;5(4):225-29. doi: 10.31661/gmj.v5i4.786.
  15. Fujii K, Yamazaki M, Kang JD, Risbud MV, Cho SK, Qureshi SA, et al. Discogenic back pain: Literature review of definition, diagnosis, and treatment. JBMR Plus. 2019;3(5):e10180. doi: 10.1002/jbm4.10180. PubMed PMID: 31131347. PubMed PMCID: PMC6524679.
  16. Kim LH, Vail D, Azad TD, Bentley JP, Zhang Y, Ho AL, et al. Expenditures and health care utilization among adults with newly diagnosed low back and lower extremity pain. JAMA Netw Open. 2019;2(5):e193676. doi: 10.1001/jamanetworkopen.2019.3676. PubMed PMID: 31074820. PubMed PMCID: PMC6512284.
  17. Soin A, Hirschbeck M, Verdon M, Manchikanti L. A Pilot Study Implementing a Machine Learning Algorithm to Use Artificial Intelligence to Diagnose Spinal Conditions. Pain Physician. 2022;25(2):171-8. PubMed PMID: 35322974.
  18. Azimi P, Yazdanian T, Benzel EC, Aghaei HN, Azhari S, Sadeghi S, et al. A review on the use of artificial intelligence in spinal diseases. Asian Spine J. 2020;14(4):543-71. doi: 10.31616/asj.2020.0147. PubMed PMID: 32326672. PubMed PMCID: PMC7435304.
  19. Das S, Sanyal MK, Datta D. Artificial intelligent embedded doctor (aiedr.): A prospect of low back pain diagnosis. International Journal of Big Data and Analytics in Healthcare (IJBDAH). 2019;4(2):34-56. doi: 10.4018/IJBDAH.2019070103.
  20. Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep. 2020;10(1):20031. doi: 10.1038/s41598-020-76866-w. PubMed PMID: 33208824. PubMed PMCID: PMC7674499.
  21. Watanabe K, Aoki Y, Matsumoto M. An application of artificial intelligence to diagnostic imaging of spine disease: Estimating spinal alignment from moire images. 2019;16(4):697-702. doi: 10.14245/ns.1938426.213. PubMed PMID: 31905459. PubMed PMCID: PMC6945007.
  22. Hirosawa T, Harada Y, Yokose M, Sakamoto T, Kawamura R, Shimizu T. Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: A pilot study. Int J Environ Res Public Health. 2023;20(4):3378. doi: 10.3390/ijerph20043378. PubMed PMID: 36834073. PubMed PMCID: PMC9967747.
  23. Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008. doi: 10.1136/bmjopen-2020-048008. PubMed PMID: 34244270. PubMed PMCID: PMC8273461.
  24. Tiwari A, Poduval M, Bagaria V. Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs. World J Orthop. 2022;13(6):603-14. doi: 10.5312/wjo.v13.i6.603. PubMed PMID: 35949704. PubMed PMCID: PMC9244962.
  25. Haeri M, Asemani D, Gharibzadeh S. Modeling of pain using artificial neural networks. J Theor Biol. 2003;220(3):277-84. doi: 10.1006/jtbi.2003.3130. PubMed PMID: 12468280.
  26. Veronezi CCD, Simões PWTDA, Santos RLD, Rocha ELD, Melão S, Mattos MCD, et al. Computational analysis based on artificial neural networks for aiding in diagnosing osteoarthritis of the lumbar spine. Rev Bras Ortop. 2015;46(2):195-9. doi: 10.1016/S2255-4971(15)30239-1. PubMed PMID: 27027010. PubMed PMCID: PMC4799207.
  27. D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadala G, et al. Artificial intelligence and computer aided diagnosis in chronic low back pain: A systematic review. Int J Environ Res Public Health. 2022;19(10):5971. doi: 10.3390/ijerph19105971. PubMed PMID: 35627508. PubMed PMCID: PMC9141006.
  28. Martin-Noguerol T, Onate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, et al. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol. 2023;161:110726. doi: 10.1016/j.ejrad.2023.110726. PubMed PMID: 36758280.
  29. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One. 2019;14(2):e0212356. doi: 10.1371/journal.pone.0212356. PubMed PMID: 30779785. PubMed PMCID: PMC6380578.
  30. Xuyi W, Seow H, Sutradhar R. Artificial neural networks for simultaneously predicting the risk of multiple co‐occurring symptoms among patients with cancer. Cancer Med. 2021;10(3):989-98. doi: 10.1002/cam4.3685. PubMed PMID: 33350595. PubMed PMCID: PMC7897969.
  31. Hao W, Cong C, Yuanfeng D, Ding W, Li J, Yongfeng S, et al. Multidata analysis based on an artificial neural network model for long-term pain outcome and key predictors of microvascular decompression in trigeminal neuralgia. World Neurosurg. 2022;164:e271-9. doi: 10.1016/j.wneu.2022.04.089. PubMed PMID: 35490890.
  32. McCartney S, Weltin M, Burchiel KJ. Use of an artificial neural network for diagnosis of facial pain syndromes: an update. Stereotact Funct Neurosurg. 2014;92(1):44-52. doi: 10.1159/000353188. PubMed PMID: 24217113.
  33. Yoon H, Bak MS, Kim SH, Lee JH, Chung G, Kim SJ, et al. Development of a spontaneous pain indicator based on brain cellular calcium using deep learning. Exp Mol Med. 2022;54(8):1179-87. doi: 10.1038/s12276-022-00828-7. PubMed PMID: 35982300. PubMed PMCID: PMC9385425.
  34. Khan O, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG. Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy. Spine J. 2021;21(10):1659-69. doi: 10.1016/j.spinee.2020.02.003. PubMed PMID: 32045708.
  35. Harvey HB, Liu C, Ai J, Jaworsky C, Guerrier CE, Flores E, et al. Predicting no-shows in radiology using regression modeling of data available in the electronic medical record. J Am Coll Radiol. 2017;14(10):1303-9. doi: 10.1016/j.jacr.2017.05.007. PubMed PMID: 28673777.
  36. Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: Current status and future directions. AJR Am J Roentgenol. 2019;213(3):506-13. doi: 10.2214/AJR.19.21117. PubMed PMID: 31166761. PubMed PMCID: PMC6706287.
  37. Yadalam PK, Trivedi SS, Krishnamurthi I, Anegundi RV, Mathew A, Al Shayeb M, et al. Machine Learning predicts patient tangible outcomes after dental implant surgery. IEEE Access. 2022;10:131481-8. doi: 10.1109/ACCESS.2022.3228793.
  38. Grimby-Ekman A, Andersson EM, Hagberg M. Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study. BMC Musculoskelet Disord. 2009;10:1-11. doi: 10.1186/1471-2474-10-73. PubMed PMID: 19545386. PubMed PMCID: PMC2714495.
  39. Segal BM, Pogatchnik B, Rhodus N, Sivils KM, McElvain G, Solid CA. Pain in primary Sjogren’s syndrome: the role of catastrophizing and negative illness perceptions. Scand J Rheumatol. 2014;43(3):234-41. doi: 10.3109/03009742.2013.846409. PubMed PMID: 24392761.
  40. Hanns L, Cordingley L, Galloway J, Norton S, Carvalho LA, Christie D, et al. Depressive symptoms, pain and disability for adolescent patients with juvenile idiopathic arthritis: results from the Childhood Arthritis Prospective Study. Rheumatology (Oxford). 2018;57(8):1381-9. doi: 10.1093/rheumatology/key088. PubMed PMID: 29697850. PubMed PMCID: PMC6055569.
  41. Joergensen AC, Hestbaek L, Andersen PK, Nybo Andersen A-M. Epidemiology of spinal pain in children: a study within the Danish National Birth Cohort. Eur J Pediatr. 2019;178(5):695-706. doi: 10.1007/s00431-019-03326-7. PubMed PMID: 30788593. PubMed PMCID: PMC6459805.
  42. Katsuki M, Narita N, Matsumori Y, Ishida N, Watanabe O, Cai S, et al. Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surg Neurol Int. 2020;11:475. doi: 10.25259/SNI_827_2020. PubMed PMID: 33500813. PubMed PMCID: PMC7827501.
  43. Dreisbach C, Koleck TA, Bourne PE, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int J Med Inform. 2019;125:37-46. doi: 10.1016/j.ijmedinf.2019.02.008. PubMed PMID: 30914179. PubMed PMCID: PMC6438188.
  44. Shwartz V, Dagan I. Still a pain in the neck: Evaluating text representations on lexical composition. Trans Assoc Comput Linguist. 2019;7:403-19. doi: 10.1162/tacl_a_00277.
  45. Kwon J, Lee H, Cho S, Chung CS, Lee MJ, Park H. Machine learning-based automated classification of headache disorders using patient-reported questionnaires. Sci Rep. 2020;10(1):14062. doi: 10.1038/s41598-020-70992-1. PubMed PMID: 32820214. PubMed PMCID: PMC7441379.
  46. Vandenbussche N, Van Hee C, Hoste V, Paemeleire K. Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache. J Headache Pain. 2022;23(1):129. doi: 10.1186/s10194-022-01490-0. PubMed PMID: 36180844. PubMed PMCID: PMC9524092.
  47. Daripa B, Lucchese S. Artificial intelligence-aided headache classification based on a set of questionnaires: A short review. 2022;14(9):e29514. doi: 10.7759/cureus.29514. PubMed PMID: 36299975. PubMed PMCID: PMC9588408.
  48. Herr K, Coyne PJ, Ely E, Gelinas C, Manworren RCB. Pain assessment in the patient unable to self-report: Clinical practice recommendations in support of the ASPMN 2019 position statement. Pain Manag Nurs. 2019;20(5):404-17. doi: 10.1016/j.pmn.2019.07.005. PubMed PMID: 31610992.
  49. Dinh-Le C, Chuang R, Chokshi S, Mann D. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR Mhealth Uhealth. 2019;7(9):e12861. doi: 10.2196/12861. PubMed PMID: 31512582. PubMed PMCID: PMC6746089.
  50. Melstrom LG, Rodin AS, Rossi LA, Fu P, Jr., Fong Y, Sun V. Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning. J Surg Oncol. 2021;123(1):52-60. doi: 10.1002/jso.26232. PubMed PMID: 32974930. PubMed PMCID: PMC7945992.
  51. Berger SE, Baria AT. Assessing pain research: A narrative review of emerging pain methods, their technosocial implications, and opportunities for multidisciplinary approaches. Front Pain Res (Lausanne). 2022;3:896276. doi: 10.3389/fpain.2022.896276. PubMed PMID: 35721658. PubMed PMCID: PMC9201034.
  52. Lotsch J, Ultsch A. Machine learning in pain research. 2018;159(4):623-30. doi: 10.1097/j.pain.0000000000001118. PubMed PMID: 29194126. PubMed PMCID: PMC5895117.
  53. Nagireddi JN, Vyas AK, Sanapati MR, Soin A, Manchikanti L. The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning. Pain Physician. 2022;25(2):E211-43. PubMed PMID: 35322975.
  54. Lin Y, Xiao Y, Wang L, Guo Y, Zhu W, Dalip B, et al. Experimental exploration of objective human pain assessment using multimodal sensing signals. Front Neurosci. 2022;16:831627. doi: 10.3389/fnins.2022.831627. PubMed PMID: 35221908. PubMed PMCID: PMC8874020.
  55. Winslow BD, Kwasinski R, Whirlow K, Mills E, Hullfish J, Carroll M. Automatic detection of pain using machine learning. Front Pain Res (Lausanne). 2022;3:1044518. doi: 10.3389/fpain.2022.1044518. PubMed PMID: 36438448. PubMed PMCID: PMC9686002.
  56. Leong SC, Tang YM, Lai CH, Lee C. Facial expression and body gesture emotion recognition: A systematic review on the use of visual data in affective computing. Comput Sci Rev. 2023;48:100545. doi: 10.1016/j.cosrev.2023.100545.
  57. Gouverneur P, Li F, Adamczyk WM, Szikszay TM, Luedtke K, Grzegorzek M. Comparison of feature extraction methods for physiological signals for heat-based pain recognition. Sensors (Basel). 2021;21(14):4838. doi: 10.3390/s21144838. PubMed PMID: 34300578. PubMed PMCID: PMC8309734.
  58. Sankaran R, Kumar A, Parasuram H. Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review. Proc Inst Mech Eng H. 2022;236(10):1478-91. doi: 10.1177/09544119221122012. PubMed PMID: 36148916.
  59. Panaggio MJ, Abrams DM, Yang F, Banerjee T, Shah NR. Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease. PLoS Comput Biol. 2021;17(3):e1008542. doi: 10.1371/journal.pcbi.1008542. PubMed PMID: 33705373. PubMed PMCID: PMC7951914.
  60. Barnes R, Zvarikova K. Artificial intelligence-enabled wearable medical devices, clinical and diagnostic decision support systems, and Internet of Things-based healthcare applications in COVID-19 prevention, screening, and treatment. American Journal of Medical Research. 2021;8(2):9-22.
  61. Barreveld AM, Rosen Klement ML, Cheung S, Axelsson U, Basem JI, Reddy AS, et al. An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain: a prospective, multicenter clinical trial. Pain Med. 2023;24(9):1100-10. doi: 10.1093/pm/pnad049. PubMed PMID: 37104747.
  62. Crawford MR, Luik AI, Espie CA, Taylor HL, Burgess HJ, Jones AL, et al. Digital cognitive behavioral therapy for insomnia in women with chronic migraines. 2020;60(5):902-15. doi: 10.1111/head.13777. PubMed PMID: 32112436.
  63. Oh J, Jang S, Kim H, Kim JJ. Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder. Int J Med Inform. 2020;140:104171. doi: 10.1016/j.ijmedinf.2020.104171. PubMed PMID: 32446158.
  64. Piette JD, Newman S, Krein SL, Marinec N, Chen J, Williams DA, et al. Patient-centered pain care using artificial intelligence and mobile health tools: A randomized comparative effectiveness trial. JAMA Intern Med. 2022;182(9):975-83. doi: 10.1001/jamainternmed.2022.3178. PubMed PMID: 35939288. PubMed PMCID: PMC9361183.
  65. Cascella M, Coluccia S, Monaco F, Schiavo D, Nocerino D, Grizzuti M, et al. Different machine learning approaches for implementing telehealth-based cancer pain management strategies. J Clin Med. 2022;11(18):5484. doi: 10.3390/jcm11185484. PubMed PMID: 36143132. PubMed PMCID: PMC9502863.
  66. Goudman L, Jansen J, Billot M, Vets N, De Smedt A, Roulaud M, et al. Virtual reality applications in chronic pain management: Systematic review and meta-analysis. JMIR Serious Games. 2022;10(2):e34402. doi: 10.2196/34402. PubMed PMID: 35536641. PubMed PMCID: PMC9131143.
  67. Shaygan M, Jaberi A. The effect of a smartphone-based pain management application on pain intensity and quality of life in adolescents with chronic pain. Sci Rep. 2021;11(1):6588. doi: 10.1038/s41598-021-86156-8. PubMed PMID: 33758322. PubMed PMCID: PMC7988051.
  68. Seneviratne MG, Li RC, Schreier M, Lopez-Martinez D, Patel BS, Yakubovich A, et al. User-centred design for machine learning in health care: a case study from care management. BMJ Health Care Inform. 2022;29(1):e100656. doi: 10.1136/bmjhci-2022-100656. PubMed PMID: 36220304. PubMed PMCID: PMC9557254.
  69. Ounajim A, Billot M, Goudman L, Louis P-Y, Slaoui Y, Roulaud M, et al. Machine learning algorithms provide greater prediction of response to SCS than lead screening trial: a predictive AI-based multicenter study. J Clin Med. 2021;10(20):4764. doi: 10.3390/jcm10204764. PubMed PMID: 34682887. PubMed PMCID: PMC8538165.
  70. Santana AN, De Santana CN, Montoya P. Chronic pain diagnosis using machine learning, questionnaires, and QST: A sensitivity experiment. Diagnostics (Basel). 2020;10(11):958. doi: 10.3390/diagnostics10110958. PubMed PMID: 33212774. PubMed PMCID: PMC7697204.
  71. Tighe PJ, Harle CA, Hurley RW, Aytug H, Boezaart AP, Fillingim RB. Teaching a machine to feel postoperative pain: combining high-dimensional clinical data with machine learning algorithms to forecast acute postoperative pain. Pain Med. 2015;16(7):1386-401. doi: 10.1111/pme.12713. PubMed PMID: 26031220. PubMed PMCID: PMC4504764.
  72. Oktay AB, Albayrak NB, Akgul YS. Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Comput Med Imaging Graph. 2014;38(7):613-9. doi: 10.1016/j.compmedimag.2014.04.006.
  73. Liu L, Zhu M-m, Cai L-l, Zhang X. Predictive models for knee pain in middle-aged and elderly individuals based on machine learning methods. Comput Math Methods Med. 2022;2022:5005195. doi: 10.1155/2022/5005195. PubMed PMID: 36199766. PubMed PMCID: PMC9529423.
  74. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Katz RS, Mease P, et al. The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity. Arthritis Care Res (Hoboken). 2010;62(5):600-10. doi: 10.1002/acr.20140. PubMed PMID: 20461783.
  75. Chattopadhyay S, Davis RM, Menezes DD, Singh G, Acharya RU, Tamura T. Application of Bayesian classifier for the diagnosis of dental pain. J Med Syst. 2012;36(3):1425-39. doi: 10.1007/s10916-010-9604-y. PubMed PMID: 20945154.
  76. Lee J, Mawla I, Kim J, Loggia ML, Ortiz A, Jung C, et al. Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. 2019;160(3):550. doi: 10.1097/j.pain.0000000000001417. PubMed PMID: 30540621. PubMed PMCID: PMC6377310.
  77. Tagliaferri SD, Wilkin T, Angelova M, Fitzgibbon BM, Owen PJ, Miller CT, et al. Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning. Sci Rep. 2022;12(1):15194. doi: 10.1038/s41598-022-19542-5. PubMed PMID: 36071092. PubMed PMCID: PMC9452567.
  78. Reis FJ, Bittencourt JV, Calestini L, de Sá Ferreira A, Meziat-Filho N, Nogueira LC. Exploratory analysis of 5 supervised machine learning models for predicting the efficacy of the endogenous pain inhibitory pathway in patients with musculoskeletal pain. Musculoskelet Sci Pract. 2023;66:102788. doi: 10.1016/j.msksp.2023.102788. PubMed PMID: 37315499.
  79. Bishop JB, Szpalski M, Ananthraman SK, McIntyre DR, Pope MH. Classification of low back pain from dynamic motion characteristics using an artificial neural network. 1997;22(24):2991-8. doi: 10.1097/00007632-199712150-00024. PubMed PMID: 9431637.
  80. Sun C, Li M, Lan L, Pei L, Zhang Y, Tan G, et al. Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches. Front Oncol. 2023;13:1096468. doi: 10.3389/fonc.2023.1096468. PubMed PMID: 36923433. PubMed PMCID: PMC10009151.
  81. Ichesco E, Peltier SJ, Mawla I, Harper DE, Pauer L, Harte SE, et al. Prediction of differential pharmacologic response in chronic pain using functional neuroimaging biomarkers and a support vector machine algorithm: an exploratory study. Arthritis Rheumatol. 2021;73(11):2127-37. doi: 10.1002/art.41781. PubMed PMID: 33982890. PubMed PMCID: PMC8597096.
  82. Fritts M, Cabrera F. AI recruitment algorithms and the dehumanization problem. Ethics Inf Technol. 2021;23:791-801. doi: 10.1007/s10676-021-09615-w.
  83. Safdar NM, Banja JD, Meltzer CC. Ethical considerations in artificial intelligence. Eur J Radiol. 2020;122:108768. doi: 10.1016/j.ejrad.2019.108768. PubMed PMID: 31786504.
  84. Hazarika I. Artificial intelligence: opportunities and implications for the health workforce. Int Health. 2020;12(4):241-5. doi: 10.1093/inthealth/ihaa007. PubMed PMID: 32300794. PubMed PMCID: PMC7322190.
  85. Naing SHH, Isaramalai S-A, Sukmag P. Policy literacy, barriers, and gender impact on accessibility to healthcare services under compulsory migrant health insurance among Myanmar migrant workers in Thailand. J Environ Public Health. 2020;2020:8165492. doi: 10.1155/2020/8165492. PubMed PMID: 33456477. PubMed PMCID: PMC7787790.
  86. Mirbabaie M, Stieglitz S, Frick NR. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 2021;11(4):693-731. doi: 10.1007/s12553-021-00555-5.
  87. Kim J, Sin M, Kim W-S, Min Y-S, Kim W, Park D, et al. Remote assessment of post-stroke elbow function using internet-based telerobotics: a proof-of-concept study. Front Neurol. 2020;11:583101. doi: 10.3389/fneur.2020.583101. PubMed PMID: 33343489. PubMed PMCID: PMC7744560.
  88. Stai B, Heller N, McSweeney S, Rickman J, Blake P, Vasdev R, et al. Public perceptions of artificial intelligence and robotics in medicine. J Endourol. 2020;34(10):1041-8. doi: 10.1089/end.2020.0137. PubMed PMID: 32611217. PubMed PMCID: PMC7578175.
  89. Shneiderman B. Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human-Computer Interaction. 2020;12(3):109-24. doi: 10.17705/1thci.00131.
Journal of Biomedical Physics and Engineering
Volume 15, Issue 4 - Serial Number 1
72
August 2025
Pages 311-322
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