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International Journal of Physiotherapy Research and Clinical Practice

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Revolutionizing Rheumatoid Arthritis Management: Harnessing the Power of Artificial Intelligence and Motor Learning Techniques for Early Detection and Personalized Interventions. A literature review
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International Journal of Physiotherapy Research and Clinical Practice

DOI: 10.54839/ijprcp.v3i4.33

Year: 2024, Volume: 3, Issue: 4, Pages: 11-15

Review Article

Revolutionizing Rheumatoid Arthritis Management: Harnessing the Power of Artificial Intelligence and Motor Learning Techniques for Early Detection and Personalized Interventions. A literature review

S Monica1, R Kaviya1, Surya Vishnuram2,∗

1Undergraduate student, Saveetha College of Physiotherapy, SIMATS, Chennai, Tamil Nadu, India
2Tutor, Saveetha College of Physiotherapy, SIMATS, Chennai, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Rheumatoid arthritis (RA) is a complex chronic autoimmune disease characterized by persistent inflammation affecting multiple joints, as well as other organs such as the heart, lungs, and kidneys. The etiology of RA remains unclear, making early detection and intervention crucial for effective disease management. The integration of Artificial Intelligence (AI) in the realm of Rheumatoid Arthritis holds promise for enabling early detection and enhancing the overall care and treatment strategies. The primary aim of this study is to highlight areas where recent research indicates the potential of Artificial Intelligence, particularly Motor Learning techniques, in improving the management of patients with Rheumatoid Arthritis. A systematic search was conducted across electronic databases to identify relevant studies. Eligibility criteria included articles focusing on AI and motor learning techniques in RA management. Four articles were finally reviewed after screening and assessment. The reviewed studies demonstrate the potential of AI algorithms in early RA detection through symptom pattern analysis and biomarker prediction. Additionally, motor learning techniques, such as virtual reality and sensor-based systems, offer innovative approaches for personalized interventions and rehabilitation in RA patients. Integration of AI and motor learning techniques shows promise in enhancing RA management strategies. Early detection facilitated by AI algorithms and personalized interventions enabled by motor learning techniques signify a transformative approach to RA care. Further research is warranted to explore the full potential of these technologies in optimizing RA management.

Keywords: Artificial intelligence, Rheumatoid Arthritis, Motor Learning techniques, Rheumatoid Arthritis Management, Rheumatoid Arthritis Treatment, Rheumatoid Arthritis Prevention, Rheumatoid Arthritis diagnosis, Newcastle-Ottawa Scale (NOS), Medical AI treatment, Artificial neural network (ANN), Early detection of Rheumatoid Arthritis, Physiotherapy, Physiotherapy Management, Rheumatoid Arthritis Physiotherapy Management, Physiotherapy Research, Recent advances in Physiotherapy, Recent Advances in Rhematoid Arithritis management, Rhematoid Arithritis diagnosis

References

  1. Klareskog L, Catrina AI, Paget S. Rheumatoid arthritis. Lancet. 2009;373(9664):659–672. Available from: https://doi.org/10.1016/S0140-6736(09)60008-8
  2. Hügle M, Omoumi P, Laar JMV, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatology advances in practice. 2020;4(1):1–10. Available from: https://doi.org/10.1093/rap/rkaa005
  3. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nature medicine. 2022;28(1):31–38. Available from: https://doi.org/10.1038/s41591-021-01614-0
  4. Stack RJ, Nightingale P, Jinks C, Shaw K, Herron-Marx S, Horne R, et al. Delays between the onset of symptoms and first rheumatology consultation in patients with rheumatoid arthritis in the UK: an observational study. BMJ open. 2019;9(3):1–8. Available from: https://doi.org/10.1136/bmjopen-2018-024361
  5. Didelot X, Pouwels KB. Machine-learning-assisted selection of antibiotic prescription. Nature medicine. 2019;25(7):1033–1034. Available from: https://doi.org/10.1038/s41591-019-0517-0
  6. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017;2(4):230–243. Available from: https://svn.bmj.com/content/2/4/230
  7. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1):80–93. Available from: https://doi.org/10.1016/j.drudis.2020.10.010
  8. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal. 2019;6(2):94–98. Available from: https://doi.org/10.7861/futurehosp.6-2-94
  9. Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. Rheumatology and Immunology Research. 2023;4(2):69–77. Available from: https://doi.org/10.2478/rir-2023-0011
  12. Mcmaster C, Bird A, Liew DF, Buchanan RR, Owen CE, Chapman WW, et al. Artificial intelligence and deep learning for rheumatologists. Arthritis & Rheumatology. 2022;74(12):1893–1905. Available from: https://doi.org/10.1002/art.42296
  13. Bai L, Zhang Y, Wang P, Zhu X, Xiong JW, Cui L. Improved diagnosis of rheumatoid arthritis using an artificial neural network. Scientific Reports. 2022;12(1):1–7. Available from: https://doi.org/10.1038/s41598-022-13750-9
  15. Mahmud M, Kaiser MS, Hussain A, Vassanelli S. Applications of deep learning and reinforcement learning to biological data. IEEE transactions on neural networks and learning systems. 2018;29(6):2063–2079. Available from: https://doi.org/10.1109/tnnls.2018.2790388
  16. Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A. How do machines learn? artificial intelligence as a new era in medicine. Journal of Personalized Medicine. 2021;11(1):1–22. Available from: https://doi.org/10.3390/jpm11010032
  17. Hügle M, Omoumi P, Laar JMV, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatology advances in practice. 2020;4(1):1–10. Available from: https://doi.org/10.1093/rap/rkaa005
  18. Maarseveen TD, Meinderink T, Reinders MJ, Knitza J, Huizinga TW, Kleyer A, et al. Machine learning electronic health record identification of patients with rheumatoid arthritis: algorithm pipeline development and validation study. JMIR medical informatics. 2020;8(11):1–12. Available from: https://doi.org/10.2196/23930

Copyright

© 2024 Published by Krupanidhi College of Physiotherapy. This is an open-access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)

  • 19 February 2025

Year: 2024, Volume: 3, Issue: 4


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