The utilization of artificial intelligence (AI) in pediatric occupational therapy (OT) has emerged as a promising avenue for enhancing assessment, intervention, and outcomes for children with diverse developmental needs. This paper provides a comprehensive review of the current state of AI applications in pediatric OT, highlighting key findings, benefits, challenges, and future directions. AI technologies, including machine learning algorithms, computer vision systems, and wearable sensors, offer innovative approaches to assess children's motor skills, sensory responses, and cognitive functions objectively and efficiently. AI-driven intervention strategies, such as personalized treatment planning, adaptive task selection, virtual reality environments, and gamified activities, promote engagement, motivation, and skill acquisition among pediatric patients. AI can be helpful in early diagnosis as well as early intervention. Additionally, AI-powered telehealth platforms enable remote delivery of OT services, real-time monitoring of patient progress, and access to care for underserved populations. However, challenges related to data privacy, ethical decision-making, disparities in access, and therapist education must be addressed to ensure the ethical, effective, and equitable integration of AI into pediatric OT practice. By embracing ongoing research, collaboration, and innovation, pediatric OT practitioners can harness the transformative potential of AI to improve outcomes and quality of life for children and families worldwide.
Published in | Rehabilitation Science (Volume 9, Issue 2) |
DOI | 10.11648/j.rs.20240902.12 |
Page(s) | 21-26 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Occupational Therapy, Artificial Intelligence, Motor Skills, Cognitive Function, Pediatric Patients, Virtual Reality
AI | Artificial Intelligence |
OT | Occupational Therapy |
BOT2 | Bruininks-Oseretsky Test of Motor Proficiency |
ASD | Autism Spectrum Disorder |
EMG | Electromyography |
VR | Virtual Reality |
HIPPA | Health Insurance Portability and Accountability Act |
[1] | Abbasgholizadeh Rahimi, S. et al. (2021) ‘Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.’, Journal of medical Internet research, 23(9), p. e29839. |
[2] | Abbasgholizadeh Rahimi, S. et al. (2022) ‘Application of Artificial Intelligence in Shared Decision Making: Scoping Review.’, JMIR medical informatics, 10(8), p. e36199. |
[3] | Adegboro, C. O. et al. (2022) ‘Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review.’, Hospital pediatrics, 12(1), pp. 93–110. |
[4] | Agadi, K. et al. (2023) ‘Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence: A Scoping Review.’, Journal of Korean Neurosurgical Society, 66(6), pp. 632–641. |
[5] | Beets, B. et al. (2023) ‘Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.’, Journal of medical Internet research, 25, p. e40337. |
[6] | Bernauer, S. A., Zitzmann, N. U. and Joda, T. (2021) ‘The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review.’, Sensors (Basel, Switzerland), 21(19). |
[7] | Bhatt, P. et al. (2022) ‘Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.’, JMIR mHealth and uHealth, 10(6), p. e35053. |
[8] | Brick, R. et al. (2022) ‘Impact of non-pharmacological interventions on activity limitations and participation restrictions in older breast cancer survivors: A scoping review.’, Journal of geriatric oncology, 13(2), pp. 132–142. |
[9] | Chew, H. S. J. and Achananuparp, P. (2022) ‘Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review.’, Journal of medical Internet research, 24(1), p. e32939. |
[10] | Choi, J., Woo, S. and Ferrell, A. (2023) ‘Artificial intelligence assisted telehealth for nursing: A scoping review.’, Journal of telemedicine and telecare, p. 1357633X231167613. |
[11] | Dabas, M. et al. (2023) ‘Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review.’, Advances in wound care, 12(4), pp. 205–240. |
[12] | Fiske, A., Henningsen, P. and Buyx, A. (2019) ‘Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy.’, Journal of medical Internet research, 21(5), p. e13216. |
[13] | Frost, E. K. et al. (2022) ‘Public views on ethical issues in healthcare artificial intelligence: protocol for a scoping review.’, Systematic reviews, 11(1), p. 142. |
[14] | Gama, F. et al. (2022) ‘Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review.’, Journal of medical Internet research, 24(1), p. e32215. |
[15] | Gehlot, V. et al. (2022) ‘Healthcare Optimization and Augmented Intelligence by Coupling Simulation & Modeling: An Ideal AI/ML Partnership for a Better Clinical Informatics.’, AMIA... Annual Symposium proceedings. AMIA Symposium, 2022, pp. 477–484. PMID: 37128375. |
[16] | von Gerich, H. et al. (2022) ‘Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence.’, International journal of nursing studies, 127, p. 104153. |
[17] | Gumbs, A. A. et al. (2021) ‘Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?’, Sensors (Basel, Switzerland), 21(16). |
[18] | Guo, Y. et al. (2020) ‘Artificial Intelligence in Health Care: Bibliometric Analysis.’, Journal of medical Internet research, 22(7), p. e18228. |
[19] | Harel-Katz, H. and Carmeli, E. (2019) ‘The association between volition and participation in adults with acquired disabilities: A scoping review.’, Hong Kong journal of occupational therapy: HKJOT, 32(2), pp. 84–96. |
[20] | Hatherly, K. et al. (2024) ‘A scoping review of virtual synchronous intervention studies in preschool rehabilitation.’, Disability and rehabilitation, 46(2), pp. 232–240. |
[21] | Kaelin, V. C. et al. (2021) ‘Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review.’, Journal of medical Internet research, 23(11), p. e25745. |
[22] | Kaelin, V. C. et al. (2022) ‘Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review.’, Frontiers in rehabilitation sciences, 3. |
[23] | Law, J. et al. (2021) ‘Tele-practice for children and young people with communication disabilities: Employing the COM-B model to review the intervention literature and inform guidance for practitioners.’, International journal of language & communication disorders, 56(2), pp. 415–434. |
[24] | Ma, B. et al. (2023) ‘Artificial intelligence in elderly healthcare: A scoping review.’, Ageing research reviews, 83, p. 101808. |
[25] | Martino, S. et al. (2022) ‘Inclusion team science improves participation of children with disabilities in pediatric obesity programs.’, Disability and health journal, 15(1), p. 101186. |
[26] | Ngombu, S. et al. (2023) ‘Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review.’, Otolaryngology--head and neck surgery: official journal of American Academy of Otolaryngology-Head and Neck Surgery, 168(4), pp. 635–642. |
[27] | Román-Belmonte, J. M., Corte-Rodríguez, H. D. la and Rodríguez-Merchán, E. C. (2021) ‘Artificial intelligence in musculoskeletal conditions.’, Frontiers in bioscience (Landmark edition), 26(11), pp. 1340–1348. |
[28] | Schachner, T., Keller, R. and V Wangenheim, F. (2020) ‘Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review.’, Journal of medical Internet research, 22(9), p. e20701. |
[29] | Shaffer, K. M. et al. (2020) ‘Dyadic Psychosocial eHealth Interventions: Systematic Scoping Review.’, Journal of medical Internet research, 22(3), p. e15509. |
[30] | Sniecinski, I. and Seghatchian, J. (2018) ‘Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine.’, Transfusion and apheresis science: official journal of the World Apheresis Association: official journal of the European Society for Haemapheresis, 57(3), pp. 422–424. |
[31] | Steinhardt, F. et al. (2022) ‘Exploring two subdimensions of participation, involvement and engagement: A scoping review.’, Scandinavian journal of occupational therapy, 29(6), pp. 441–463. |
[32] | Vishwanathaiah, S. et al. (2023) ‘Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review.’, Biomedicines, 11(3). |
[33] | Visram, S. et al. (2023) ‘Engaging children and young people on the potential role of artificial intelligence in medicine.’, Pediatric research, 93(2), pp. 440–444. |
[34] | Xie, B. et al. (2020) ‘Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review.’, JMIR medical informatics, 8(8), p. e18189. |
[35] | Yoo, P. Y. et al. (2022) ‘The Effect of Context-Based Interventions at the Systems-Level on Participation of Children with Disabilities: A Systematic Review.’, Physical & occupational therapy in pediatrics, 42(5), pp. 542–565. |
APA Style
Sharma, N. (2024). Use of AI in Pediatric Occupational Therapy: A Review. Rehabilitation Science, 9(2), 21-26. https://doi.org/10.11648/j.rs.20240902.12
ACS Style
Sharma, N. Use of AI in Pediatric Occupational Therapy: A Review. Rehabil. Sci. 2024, 9(2), 21-26. doi: 10.11648/j.rs.20240902.12
AMA Style
Sharma N. Use of AI in Pediatric Occupational Therapy: A Review. Rehabil Sci. 2024;9(2):21-26. doi: 10.11648/j.rs.20240902.12
@article{10.11648/j.rs.20240902.12, author = {Nirvi Sharma}, title = {Use of AI in Pediatric Occupational Therapy: A Review }, journal = {Rehabilitation Science}, volume = {9}, number = {2}, pages = {21-26}, doi = {10.11648/j.rs.20240902.12}, url = {https://doi.org/10.11648/j.rs.20240902.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.rs.20240902.12}, abstract = {The utilization of artificial intelligence (AI) in pediatric occupational therapy (OT) has emerged as a promising avenue for enhancing assessment, intervention, and outcomes for children with diverse developmental needs. This paper provides a comprehensive review of the current state of AI applications in pediatric OT, highlighting key findings, benefits, challenges, and future directions. AI technologies, including machine learning algorithms, computer vision systems, and wearable sensors, offer innovative approaches to assess children's motor skills, sensory responses, and cognitive functions objectively and efficiently. AI-driven intervention strategies, such as personalized treatment planning, adaptive task selection, virtual reality environments, and gamified activities, promote engagement, motivation, and skill acquisition among pediatric patients. AI can be helpful in early diagnosis as well as early intervention. Additionally, AI-powered telehealth platforms enable remote delivery of OT services, real-time monitoring of patient progress, and access to care for underserved populations. However, challenges related to data privacy, ethical decision-making, disparities in access, and therapist education must be addressed to ensure the ethical, effective, and equitable integration of AI into pediatric OT practice. By embracing ongoing research, collaboration, and innovation, pediatric OT practitioners can harness the transformative potential of AI to improve outcomes and quality of life for children and families worldwide.}, year = {2024} }
TY - JOUR T1 - Use of AI in Pediatric Occupational Therapy: A Review AU - Nirvi Sharma Y1 - 2024/09/11 PY - 2024 N1 - https://doi.org/10.11648/j.rs.20240902.12 DO - 10.11648/j.rs.20240902.12 T2 - Rehabilitation Science JF - Rehabilitation Science JO - Rehabilitation Science SP - 21 EP - 26 PB - Science Publishing Group SN - 2637-594X UR - https://doi.org/10.11648/j.rs.20240902.12 AB - The utilization of artificial intelligence (AI) in pediatric occupational therapy (OT) has emerged as a promising avenue for enhancing assessment, intervention, and outcomes for children with diverse developmental needs. This paper provides a comprehensive review of the current state of AI applications in pediatric OT, highlighting key findings, benefits, challenges, and future directions. AI technologies, including machine learning algorithms, computer vision systems, and wearable sensors, offer innovative approaches to assess children's motor skills, sensory responses, and cognitive functions objectively and efficiently. AI-driven intervention strategies, such as personalized treatment planning, adaptive task selection, virtual reality environments, and gamified activities, promote engagement, motivation, and skill acquisition among pediatric patients. AI can be helpful in early diagnosis as well as early intervention. Additionally, AI-powered telehealth platforms enable remote delivery of OT services, real-time monitoring of patient progress, and access to care for underserved populations. However, challenges related to data privacy, ethical decision-making, disparities in access, and therapist education must be addressed to ensure the ethical, effective, and equitable integration of AI into pediatric OT practice. By embracing ongoing research, collaboration, and innovation, pediatric OT practitioners can harness the transformative potential of AI to improve outcomes and quality of life for children and families worldwide. VL - 9 IS - 2 ER -