Advancement, utilization, and future outlook of Artificial Intelligence for physiotherapy clinical trials in India: An overview
DOI:
https://doi.org/10.56294/ri202473Keywords:
Artificial Intelligence Future, Physiotherapy, Clinical Trials, IndiaAbstract
As healthcare landscapes evolve, Artificial intelligence (AI) has emerged as a transformative force in physiotherapy research in India. The integration of machine learning algorithms, computer vision, and natural language processing has significantly advanced the analysis of patient data, enabling the prediction of treatment outcomes and personalization of physiotherapy interventions. This overview delves into specific examples of successful AI integration in ongoing clinical trials within the Indian context, showcasing notable improvements in trial efficiency and positive impacts on patient outcomes. Challenges in implementing AI, including data security, ethical considerations, and the need for specialized training, are discussed. Proposed solutions encompass robust data encryption, ethical guidelines, interpretability of AI models, and targeted educational programs for healthcare professionals. Looking forward, the future outlook emphasizes personalized treatment plans, expanded tele physiotherapy using wearable technology, and the integration of augmented and virtual reality. Ethical and regulatory frameworks, continued advancements in robotic assistance, and interdisciplinary collaboration are highlighted as key factors shaping the trajectory of AI in physiotherapy clinical trials in India. The primary objectives of this manuscript are to explore the current state of AI in physiotherapy clinical trials in India, assess its utilization, and discuss the potential future developments in the field.
References
1. Tack C. Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice 2018. https://doi.org/10.1016/j.msksp.2018.11.012.
2. Wright A, Stone K, Martinelli L, Fryer S, Smith G, Lambrick D, et al. Effect of combined home-based, overground robotic-assisted gait training and usual physiotherapy on clinical functional outcomes in people with chronic stroke: A randomized controlled trial. Clinical Rehabilitation 2021;35:882-93. https://doi.org/10.1177/0269215520984133.
3. Sánchez CMC, León LAG, Yanes RCA, Oloriz MAG. Metaverse: the future of medicine in a virtual world. Metaverse Basic and Applied Research 2022;1:4-4. https://doi.org/10.56294/mr20224.
4. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health and Technology 2023;13:203-13. https://doi.org/10.1007/s12553-023-00738-2.
5. Inastrilla CRA. Data Visualization in the Information Society. Seminars in Medical Writing and Education 2023;2:25-25. https://doi.org/10.56294/mw202325.
6. Pool D, Valentine J, Taylor NF, Bear N, Elliott C. Locomotor and robotic assistive gait training for children with cerebral palsy 2020. https://doi.org/10.1111/dmcn.14746.
7. Harrison CJ, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction to natural language processing. BMC Medical Research Methodology 2021;21:1-18. https://doi.org/10.1186/s12874-021-01347-1.
8. Zhang W. Blockchain-based solutions for clinical trial data management: a systematic review. Metaverse Basic and Applied Research 2022;1:17-17. https://doi.org/10.56294/mr202217.
9. Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, et al. Correction to: The role of machine learning in clinical research: transforming the future of evidence generation (Trials, (2021), 22, 1, (537), 10.1186/s13063-021-05489-x). Trials 2021;22:1-15. https://doi.org/10.1186/s13063-021-05571-4.
10. Rodríguez FAR, Flores LG, Vitón-Castillo AA. Artificial intelligence and machine learning: present and future applications in health sciences. Seminars in Medical Writing and Education 2022;1:9-9. https://doi.org/10.56294/mw20229.
11. Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. npj Digital Medicine 2019;2. https://doi.org/10.1038/s41746-019-0148-3.
12. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 2019;17:1-9. https://doi.org/10.1186/s12916-019-1426-2.
13. Farhud DD, Zokaei S. Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health 2021;50:i-v. https://doi.org/10.18502/ijph.v50i11.7600.
14. Canova-Barrios C, Machuca-Contreras F. Interoperability standards in Health Information Systems: systematic review. Seminars in Medical Writing and Education 2022;1:7-7. https://doi.org/10.56294/mw20227.
15. Rojas-Avila J, Reynaldos-Grandón K. Intercambio de datos secundarios en la investigación con seres humanos: Aspectos éticos. Salud, Ciencia y Tecnología 2023;3:432-432. https://doi.org/10.56294/saludcyt2023432.
16. Varga G, Stoicu-Tivadar L, Nicola S. Serious Gaming and Artificial Intelligence in Rehabilitation of Rheumatoid Arthritis. Studies in Health Technology and Informatics 2022;295:562-5. https://doi.org/10.3233/SHTI220790.
17. Gudhe V, Qureshi MI, Kovela RK. A Randomized Controlled Trial to Study the Effectiveness of Interactive, Virtual Tele-Physiotherapy for Improving Motor Function and Quality of Life in Stroke Patients: A Study Protocol. Journal of Pharmaceutical Research International 2021;33:187-93. https://doi.org/10.9734/jpri/2021/v33i51B33530.
18. Díaz-Chieng LY, Auza-Santiváñez JC, Castillo JIR. The future of health in the metaverse. Metaverse Basic and Applied Research 2022;1:1-1. https://doi.org/10.56294/mr20221.
19. Tschuggnall M, Grote V, Pirchl M, Holzner B, Rumpold G, Fischer MJ. Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures. Informatics in Medicine Unlocked 2021;24:100598. https://doi.org/10.1016/j.imu.2021.100598.
20. Debnath B, O’Brien M, Yamaguchi M, Behera A. A review of computer vision-based approaches for physical rehabilitation and assessment. vol. 28. Springer Berlin Heidelberg; 2022. https://doi.org/10.1007/s00530-021-00815-4.
21. Barrios CJC. Aspectos éticos en la publicación de manuscritos científicos: Una revisión de literatura. Salud, Ciencia y Tecnología 2022;2:81-81. https://doi.org/10.56294/saludcyt202281.
22. Inastrilla CRA. Big Data in Health Information Systems. Seminars in Medical Writing and Education 2022;1:6-6. https://doi.org/10.56294/mw20226.
23. Zhang C. Intelligent Internet of things service based on artificial intelligence technology. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, p. 731-4. https://doi.org/10.1109/ICBAIE52039.2021.9390061.
24. Ma B, Yang J, Wong FKY, Wong AKC, Ma T, Meng J, et al. Artificial intelligence in elderly healthcare: A scoping review. Ageing Research Reviews 2023;83:101808. https://doi.org/10.1016/j.arr.2022.101808.
25. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 2021;21:125. https://doi.org/10.1186/s12911-021-01488-9.
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