NAVIGATING AI IMPLEMENTATION: A THREE-DIMENSIONAL FRAMEWORK FOR PEOPLE WITH DISABILITIES IN MALAYSIAN COMMUNITY-BASED REHABILITATION
DOI:
https://doi.org/10.55197/qjssh.v7i1.947Keywords:
artificial intelligence, community-based rehabilitation, persons with disabilities, conceptual framework, diagnostic toolAbstract
Artificial Intelligence offers significant potential to enhance disability support, yet its adoption in Malaysia’s Community-based Rehabilitation system remains inconsistent due to diverse user needs, limited infrastructure, and uneven practitioner readiness. In Malaysia, Community-based Rehabilitation practitioners lack systematic tools for Artificial Intelligence adoption, leading to wasted resources and technology abandonment. This study conducted a systematic literature review of 27 studies published between 2020 and 2025, following PRISMA guidelines, to examine Artificial Intelligence-enabled interventions for persons with disabilities and identify determinants of successful community implementation. Evidence shows that effective Artificial Intelligence deployment depends on three interdependent domains which are specific functional needs, participation-focused life goals, and contextual enablers such as connectivity, device access, workforce capacity, governance, and affordability. These domains consistently shape feasibility, safety, and real-world impact across healthcare, education, and rehabilitation settings. Based on these findings, the paper introduces a Three-Dimensional Framework that positions a person with disabilities’ assessment within a functional-life-context diagnostic space to guide Artificial Intelligence prescription in Community-based Rehabilitation centres. The model provides a practical, evidence-based tool for matching technology to individual needs while accounting for environmental realities, thereby improving decision-making, reducing technology abandonment, and supporting equitable Artificial Intelligence adoption in Malaysian community rehabilitation.
References
[1] Adenan, N.D., Abdullah, N., Mohamad Said, N.S., Abdul Manaf, S.M., Mohd Zahari, A.S. (2023): Community-Based Rehabilitation: An Overview of Organisational Practices. – International Journal of Academic Research in Business and Social Sciences 13(11): 2758-2767.
[2] Ahmed, A.N., Nuri, R.P., Xu, X., Balakrishna, V., Sebeh, A., Maholo, C., Aldersey, H.M. (2024): Community-based rehabilitation/community based inclusive development functioning during the COVID-19 pandemic: A secondary analysis of qualitative data. – PLOS ONE 19(1): 1-12.
[3] Alouthah, M.G.S., Alshammari, M.F.N., Alsaadi, H.F.F., Alshammari, S.N.K., Alonayzan, H.H.M., Alenezi, T.S.S., Alotaibi, M.S.A., Alghanim, H.A., Alharbi, T.A., Albarrak, O.S.M., Alshammari, M.A., Altheyab, S.A., Almutrfy, M.A., Alharbi, M.S.S., Alharbi, S.S. (2024): Advanced technologies in rehabilitation programs: Role of AI in diagnosis-an updated review. – International Journal of Health Sciences 8(S1): 1588-1604.
[4] Amdan, M.A., Janius, N., Jasman, M.N.B., Kasdiah, M.A.H.B. (2024): Advancement of ai-tools in learning for technical vocational education and training (TVET) in Malaysia (empowering students and tutor). – International Journal of Science and Research Archive 12(1): 2061-2068.
[5] Apostolidis, K., Kokkotis, C., Moustakidis, S., Karakasis, E., Sakellari, P., Koutra, C., Tsiptsios, D., Karatzetzou, S., Vadikolias, K., Aggelousis, N. (2023): Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review. – Human-Centric Intelligent Systems 4(1): 147-160.
[6] Arends, B.K.O., McCormick, J.M., Van Der Harst, P., Heus, P., Van Es, R. (2025): Barriers, facilitators and strategies for the implementation of artificial intelligence‐based electrocardiogram interpretation: A mixed‐methods study. – European Journal of Clinical Investigation 55(S1): 1-10.
[7] Arifin, N.N.M., Hassan, A.C., Musman, M., Nik Wil, N.J. (2025): Breaking Barriers: Empowering Special Needs Accountants in Malaysia through Artificial Intelligence and Assistive Technology. – Information Management and Business Review 17(3(I)S): 326-334.
[8] Banyai, A.D., Brișan, C. (2024): Robotics in Physical Rehabilitation: Systematic Review. – Healthcare 12(17): 1-17.
[9] Bokalial, D., Hossain, Md. F., Kumar, N.S.S., Bajracharya, S. (2020): Effectiveness of Community-Based Rehabilitation on the lives of Parents of Children with Cerebral Palsy: A Mixed Method Study in Karnataka, India. – Disability, CBR & Inclusive Development 31(3): 23-45.
[10] Boltaboyeva, A., Baigarayeva, Z., Imanbek, B., Ozhikenov, K., Getahun, A.J., Aidarova, T., Karymsakova, N. (2025): A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring. – Applied Sciences 15(12): 1-28.
[11] Bonanno, L., Cannuli, A., Pignolo, L., Marino, S., Quartarone, A., Calabrò, R.S., Cerasa, A. (2023): Neural Plasticity Changes Induced by Motor Robotic Rehabilitation in Stroke Patients: The Contribution of Functional Neuroimaging. – Bioengineering 10(8): 1-22.
[12] Bulan, P.M.P., Kuizon, D.A.Y., Casaña, R.S.E., Fuentes, C.G., Pestaño, N.Y., Suerte, J.R.O. (2025): A Scoping Review on Artificial Intelligence in Occupational Therapy. – Occupational Therapy Journal of Research 15p.
[13] Bzikowska-Jura, A., Sobieraj, P., Raciborski, F. (2021): Low Comparability of Nutrition-Related Mobile Apps against the Polish Reference Method-A Validity Study. – Nutrients 13(8): 1-18.
[14] Campagnini, S., Arienti, C., Patrini, M., Liuzzi, P., Mannini, A., Carrozza, M.C. (2022): Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: A systematic review. – Journal of NeuroEngineering and Rehabilitation 19(1): 1-22.
[15] Chin, C.S., Piragasam, G.A. (2021): Adoption of DETB with SRP amongst VIP at Tertiary Institutions in Malaysia. – International Journal of Academic Research in Progressive Education and Development 10(2): 24-34.
[16] Chomutare, T., Tejedor, M., Svenning, T.O., Marco-Ruiz, L., Tayefi, M., Lind, K., Godtliebsen, F., Moen, A., Ismail, L., Makhlysheva, A., Ngo, P.D. (2022): Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators. – International Journal of Environmental Research and Public Health 19(23): 1-18.
[17] Damschroder, L.J., Reardon, C.M., Widerquist, M.A.O., Lowery, J. (2022): The updated Consolidated Framework for Implementation Research based on user feedback. – Implementation Science 17(1): 1-16.
[18] De Freitas, M.P., Piai, V.A., Farias, R.H., Fernandes, A.M.R., De Moraes Rossetto, A.G., Leithardt, V.R.Q. (2022): Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review. – Sensors 22(21): 1-20.
[19] Denecke, K., Baudoin, C.R. (2022): A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. – Frontiers in Medicine 9: 1-13.
[20] Epalte, K., Grjadovojs, A., Bērziņa, G. (2023): Use of the Digital Assistant Vigo in the Home Environment for Stroke Recovery: Focus Group Discussion With Specialists Working in Neurorehabilitation. – JMIR Rehabilitation and Assistive Technologies 10: 1-14.
[21] Eziamaka, N.V., Odonkor, T.N., Akinsulire, A.A. (2024): AI-Driven accessibility: Transformative software solutions for empowering individuals with disabilities. – International Journal of Applied Research in Social Sciences 6(8): 1612-1641.
[22] Fujimori, R., Liu, K., Soeno, S., Naraba, H., Ogura, K., Hara, K., Sonoo, T., Ogura, T., Nakamura, K., Goto, T. (2022): Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation. – JMIR Formative Research 6(6): 1-12.
[23] Guo, A., Kamar, E., Vaughan, J.W., Wallach, H., Morris, M.R. (2020): Toward Fairness in Ai for People with Disabilities: A Research Roadmap. – ACM SIGACCESS Accessibility and Computing 125: 1p.
[24] Guo, K., Orban, M., Lu, J., Al-Quraishi, M.S., Yang, H., Elsamanty, M. (2023): Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System. – Bioengineering 10(5): 1-18.
[25] Hakiki, B., Donnini, I., Romoli, A.M., Draghi, F., Maccanti, D., Grippo, A., Scarpino, M., Maiorelli, A., Sterpu, R., Atzori, T., Mannini, A. (2022): Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study. – Frontiers in Neurology 13: 1-12.
[26] Hasan, H., Abdul Aziz, A.F., Aljunid, S.M. (2021): Parents’ and caregivers’ satisfaction with community‐based rehabilitation (CBR) services for children with disability in east coast states in Peninsular Malaysia. – Health & Social Care in the Community 29(1): 215-226.
[27] Hurtubise, K., Gaboury, I., Berbari, J., Battista, M.C., Schuster, T., Phoenix, M., Rosenbaum, P., De Camargo, O.K., Lovo, S., Pritchard-Wiart, L., Zwicker, J.G. (2022): Training Intervention and Program of Support for Fostering the Adoption of Family-Centered Telehealth in Pediatric Rehabilitation: Protocol for a Multimethod, Prospective, Hybrid Type 3 Implementation-Effectiveness Study. – JMIR Research Protocols 11(10): 1-16.
[28] Iberahim, M.A.I., Shamsuddin, S.N.W., Makhtar, M., El-Ebiary, Y.A.B. (2024): Enhancement Of Finger State Progress Model for Markerless Virtual Fine Motor Stroke Rehabilitation. – Nanotechnology Perceptions 20(S14): 1025-1034.
[29] Istiyati, S., Marmoah, S., Poerwanti, J.I.S., Supianto, Sukarno, Mahfud, H. (2023): Comparative Study of Education for Children with Special Needs in Malaysia and Indonesian Primary School. – Jurnal Penelitian Pendidikan IPA 9(10): 7903-7908.
[30] Jabatan Kebajikan Masyarakat (JKM) (2023): Laporan Statistik Tahunan PDK 2022. – Jabatan Kebajikan Masyarakat, Kementerian Pembangunan Wanita, Keluarga dan Masyarakat 270p.
[31] Jeter, R., Greenfield, R., Housley, S.N., Belykh, I. (2024): Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach. – JMIR Biomedical Engineering 9: 1-15.
[32] Kaelin, V.C., Valizadeh, M., Salgado, Z., Parde, N., Khetani, M.A. (2021): Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review. – Journal of Medical Internet Research 23(11): 1-17.
[33] Kamrozzaman, N.A., Sabtu, S.A., Xiu Jie, A.L., Md Nayan, N.S. (2025): Barriers to Artificial Intelligence Adoption in the Malaysian Virtual Assistant Industry: A Mixed-Methods Study. – International Journal of Research and Innovation in Social Science IX(V): 3722-3732.
[34] Kim, J., Kim, S., Rhee, J., Lee, Y., Park, J. (2024): Exploring Information Accessibility Needs for Individuals with Visual Impairments in Digital Sophistication: Focusing on Welfare Center, Enterprise, and Government Policy. – Korean Society for Rehabilitation of Persons with Disabilities 28(4): 99-129.
[35] Koshy, D.I., Koshy, D., Joseph, K.V. (2025): Trauma Care Provision in Malaysia: Challenges, Strengths, and Strategic Priorities for System Reform. – Cureus 6p.
[36] Latif, G., Brahim, G.B., Abdelhamid, S.E., Alghazo, R., Alhabib, G., Alnujaidi, K. (2023): Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System. – Applied System Innovation 6(5): 1-18.
[37] Liscano, Y., Bernal, L.M., Díaz Vallejo, J.A. (2025): Effectiveness of AI-Assisted Digital Therapies for Post-Stroke Aphasia Rehabilitation: A Systematic Review. – Brain Sciences 15(9): 1-20.
[38] Lourenço, J.W., Jesus, P.A.C.D., Lourenço, F., Canciglieri Junior, O., Schaefer, J.L. (2025): A Systematic Review on Assistive Technology Terminologies, Concepts, and Definitions. – Technologies 13(8): 1-20.
[39] Lugo-Agudelo, L., Sarmiento, K., Brunal, M., Correa, J., Borrero, A., Franco, L., Ianini, R., Lis, P., Vélez, C., Lugo, D., Gutenbrunner, C. (2021): Adaptations for rehabilitation services during the COVID-19 pandemic proposed by scientific organizations and rehabilitation professionals. – Journal of Rehabilitation Medicine 53(9): 1-10.
[40] Manirajee, L., Hanis Shariff, S.Q., Mohd Rashid, S.M. (2024): Assistive Technology for Visually Impaired Individuals: A Systematic Literature Review (SLR). – International Journal of Academic Research in Business and Social Sciences 14(2): 596-611.
[41] Matheny, M.E., Whicher, D., Thadaney Israni, S. (2020): Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. – JAMA 323(6): 509-510.
[42] Mennella, C., Maniscalco, U., De Pietro, G., Esposito, M. (2023): The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review. – IEEE Access 11: 11024-11043.
[43] Mohammad Namdar, M., Lowery Wilson, M., Murtonen, K.P., Aartolahti, E., Oduor, M., Korniloff, K. (2025): How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review. – JMIR Rehabilitation and Assistive Technologies 12: 1-13.
[44] Newman-Griffis, D.R., Desmet, B., Zirikly, A., Tamang, S., Chang, C.H. (2023): Editorial: Artificial intelligence for human function and disability. – Frontiers in Digital Health 5: 1-3.
[45] Noukpo, S.I., Kossi, O., Tedesco Triccas, L., Adoukonou, T., Feys, P. (2022): Content and Effectiveness of Community-Based Rehabilitation on Quality of Life in People Post Stroke: A Systematic Review with Meta-Analysis. – Disability, CBR & Inclusive Development 33(2): 345-345.
[46] Pancholi, S., Wachs, J.P., Duerstock, B.S. (2024): Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities. – Annual Review of Biomedical Engineering 26(1): 1-24.
[47] Rehman, I.U., Sobnath, D., Nasralla, M.M., Winnett, M., Anwar, A., Asif, W., Sherazi, H.H.R. (2021): Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI. – Diagnostics 11(10): 1-20.
[48] Rowland, S.P., Fitzgerald, J.E., Holme, T., Powell, J., McGregor, A. (2020): What is the clinical value of mHealth for patients? – NPJ Digital Medicine 3(1): 1-6.
[49] Sangers, T.E., Nijsten, T., Wakkee, M. (2021): Mobile health skin cancer risk assessment campaign using artificial intelligence on a population‐wide scale: A retrospective cohort analysis. – Journal of the European Academy of Dermatology and Venereology 35(11): 772-774.
[50] Shmonin, A.A., Maltseva, M.N., Soloveva, L.N., Melnikova, E.V. (2023): Comparison of multidisciplinary use of the International Classification of Functioning (ICF) in paper and electronic forms for patients with acute stroke. – Regional Blood Circulation and Microcirculation 22(2): 44-50.
[51] Shukla, K. (2022): Access to assistive technology for people with visual impairment. – IHOPE Journal of Ophthalmology 1: 79-85.
[52] Smith, P., Smith, L. (2021): Artificial intelligence and disability: Too much promise, yet too little substance? – AI and Ethics 1(1): 81-86.
[53] Soares, A.N., Benetti, M. (2025): Low-Cost Assistive Technology at the Cross-Cut of Education and Health. – Revista de Gestão Social e Ambiental 19(5): 1-16.
[54] Tofani, M., Esposito, G., Berardi, A., Galeoto, G., Iorio, S., Marceca, M. (2021): Community-Based Rehabilitation Indicators: Validation and Preliminary Evidence for Disability in Italy. – International Journal of Environmental Research and Public Health 18(21): 1-10.
[55] Trani, J.F., Pitzer, K.A., Vasquez Escallon, J., Bakhshi, P. (2022): Access to Services from Persons with Disabilities in Afghanistan: Is Community Based Rehabilitation Making a Difference? – International Journal of Environmental Research and Public Health 19(10): 1-26.
[56] Trani, J.F., Vasquez-Escallon, J., Bakhshi, P. (2021): The impact of a community based rehabilitation program in Afghanistan: A longitudinal analysis using propensity score matching and difference in difference analysis. – Conflict and Health 15(1): 1-21.
[57] Umucu, E. (2025): Artificial Intelligence and Health Equity for People with Disabilities: An Integrated Framework for Disability-Inclusive AI Design. – INQUIRY: The Journal of Health Care Organization, Provision, and Financing 62: 1-6.
[58] Valentine, A.Z., Hall, S.S., Young, E., Brown, B.J., Groom, M.J., Hollis, C., Hall, C.L. (2021): Implementation of Telehealth Services to Assess, Monitor, and Treat Neurodevelopmental Disorders: Systematic Review. – Journal of Medical Internet Research 23(1): 1-14.
[59] Vélez-Guerrero, M.A., Callejas-Cuervo, M., Mazzoleni, S. (2021): Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. – Sensors 21(6): 1-29.
[60] Vogan, A.A., Alnajjar, F., Gochoo, M., Khalid, S. (2020): Robots, AI, and Cognitive Training in an Era of Mass Age-Related Cognitive Decline: A Systematic Review. – IEEE Access 8: 18284-18304.
[61] Wang, X., Wang, B., Tew, W.Y., Yang, X., Xu, X., Gao, Y., Chen, Y., Yam, M.F. (2025): Exploring mHealth interventions for medication management: A scoping review of digital tools, implementation barriers, and patient outcomes. – PeerJ Computer Science 11: 1-34.
[62] Wiljer, D., Salhia, M., Dolatabadi, E., Dhalla, A., Gillan, C., Al-Mouaswas, D., Jackson, E., Waldorf, J., Mattson, J., Clare, M., Lalani, N., Charow, R., Balakumar, S., Younus, S., Jeyakumar, T., Peteanu, W., Tavares, W. (2021): Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach. – JMIR Research Protocols 10(10): 1-14.
[63] World Health Organization and the United Nations Children’s Fund (UNICEF) (2022): Global report on assistive technology. – UNICEF 7p.
[64] Xue, W., Duan, Ai., Zhao, Y., Zuo, H., Isleem, H.F. (2025): AI-enabled multimodal monitoring for enhanced safety and functional recovery in elderly and post-stroke care: A mixed-methods study. – DIGITAL HEALTH 11: 1-34.
[65] Yunus, F.W., Ahmad Ridhuwan, N.F., Romli, M.H. (2022): The Perception of Allied Health Professionals on Occupational Therapy. – Occupational Therapy International 8p.
[66] Zaidi, M.F.A. (2025): Understanding AI Ecosystem towards Sustainability in Malaysia. – Global Conference on Business and Social Sciences Proceeding 17(1): 69-69.
[67] Zainal, A.Z., Zainuddin, S.Z. (2020): Technology adoption in Malaysian schools: An analysis of national ICT in education policy initiatives. – Digital Education Review 37: 172-194.
[68] Zdravkova, K., Krasniqi, V., Dalipi, F., Ferati, M. (2022): Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective. – Frontiers in Artificial Intelligence 5: 1-14.
[69] Ziebland, S., Hyde, E., Powell, J. (2021): Power, paradox and pessimism: On the unintended consequences of digital health technologies in primary care. – Social Science & Medicine 289: 1-24.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ZOEL FAZLEE OMAR, MIOR HARRIS MIOR HARUN, NOR IRVONI MOHD ISHAR, NUR ARFAH MUSTAPHA, ZURINA ISMAIL
This work is licensed under a Creative Commons Attribution 4.0 International License.
