Comparative Effectiveness of AI-Assisted Telerehabilitation Versus Conventional Rehabilitation for Upper Limb Recovery After Stroke: A Systematic Review and Meta-Analysis
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Published:
Jul 24, 2026
Keywords:
artificial intelligence, motor recovery, stroke, telerehabilitation, upper limb
Main Article Content
Abstract
ABSTRACT
Background: Stroke is a leading cause of long-term disability, with upper limb motor deficits limiting functional independence and quality of life. Advances in artificial intelligence (AI) have enabled AI-assisted telerehabilitation platforms that deliver intensive, task-specific, individualized therapy remotely, yet their comparative effectiveness remains uncertain.
Methods: PubMed, Embase, Scopus, Cochrane CENTRAL, and Web of Science were searched to June 2025. Randomized controlled trials and quasi-experimental studies enrolling adults with stroke-related upper limb impairment were eligible. Studies compared AI-assisted telerehabilitation with conventional therapist-led or standard remote therapy and reported validated outcomes, including the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Motor Activity Log (MAL). Risk of bias was assessed using the Cochrane tool, and pooled mean differences were calculated using random-effects models.
Result: Five studies including 339 participants met inclusion criteria. Meta-analysis demonstrated no statistically significant difference between AI-assisted telerehabilitation and conventional therapy. The pooled mean difference for FMA-UE was 0.55 (95% CI: -0.60 to 1.08; p=0.38; I²=89%), and for MAL was 0.36 (95% CI: -0.45 to 1.17; p=0.39). Both groups achieved clinically meaningful improvements, with no serious adverse events reported.
Conclusion: AI-assisted telerehabilitation is an alternative to conventional post-stroke upper limb rehabilitation.
Keywords: artificial intelligence, motor recovery, stroke, telerehabilitation, upper limb
ABSTRAK
Latar Belakang: Stroke merupakan salah satu penyebab disabilitas jangka panjang, dengan gangguan fungsi motorik ekstremitas atas membatasi kemandirian fungsional serta kualitas hidup. Telerehabilitasi berbasis AI sebagai alternatif layanan rehabilitasi memungkinkan pemberian terapi secara intensif, spesifik terhadap tugas, dan terindividualisasi dari rumah. Namun, bukti mengenai efektivitas telerehabilitasi berbasis AI masih terbatas.
Metode: Pencarian literatur dilakukan pada PubMed, Embase, Scopus, Cochrane CENTRAL, dan Web of Science hingga Juni 2025. Studi uji acak terkontrol dan kuasi-eksperimental yang melibatkan pasien dewasa pascastroke dengan gangguan ekstremitas atas disertakan. Studi membandingkan telerehabilitasi berbasis AI dengan terapi konvensional yang dipandu terapis atau terapi jarak jauh standar, disertai luaran tervalidasi seperti Fugl-Meyer Assessment untuk Ekstremitas Atas (FMA-UE) dan Motor Activity Log (MAL). Penilaian risiko bias dilakukan menggunakan alat Cochrane, dan analisis meta dilakukan dengan model efek acak.
Hasil: Lima studi dengan total 339 partisipan memenuhi kriteria inklusi. Hasil meta-analisis menunjukkan tidak terdapat perbedaan bermakna secara statistik antara kelompok telerehabilitasi berbasis AI dan terapi konvensional. Perbedaan rerata gabungan FMA-UE adalah 0.55 (95% CI: -0.60 to 1.08; p=0.38; I²=89%), sedangkan untuk MAL sebesar 0.36 (95% CI: -0.45 to 1.17; p=0.39). Kedua kelompok menunjukkan perbaikan klinis bermakna, tanpa kejadian efek samping serius.
Kesimpulan: Telerehabilitasi berbasis AI merupakan alternatif yang layak untuk pemulihan fungsi ekstremitas atas pascastroke.
Kata kunci: kecerdasan buatan, pemulihan motorik, stroke, telerehabilitasi, ekstremitas atas
Background: Stroke is a leading cause of long-term disability, with upper limb motor deficits limiting functional independence and quality of life. Advances in artificial intelligence (AI) have enabled AI-assisted telerehabilitation platforms that deliver intensive, task-specific, individualized therapy remotely, yet their comparative effectiveness remains uncertain.
Methods: PubMed, Embase, Scopus, Cochrane CENTRAL, and Web of Science were searched to June 2025. Randomized controlled trials and quasi-experimental studies enrolling adults with stroke-related upper limb impairment were eligible. Studies compared AI-assisted telerehabilitation with conventional therapist-led or standard remote therapy and reported validated outcomes, including the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Motor Activity Log (MAL). Risk of bias was assessed using the Cochrane tool, and pooled mean differences were calculated using random-effects models.
Result: Five studies including 339 participants met inclusion criteria. Meta-analysis demonstrated no statistically significant difference between AI-assisted telerehabilitation and conventional therapy. The pooled mean difference for FMA-UE was 0.55 (95% CI: -0.60 to 1.08; p=0.38; I²=89%), and for MAL was 0.36 (95% CI: -0.45 to 1.17; p=0.39). Both groups achieved clinically meaningful improvements, with no serious adverse events reported.
Conclusion: AI-assisted telerehabilitation is an alternative to conventional post-stroke upper limb rehabilitation.
Keywords: artificial intelligence, motor recovery, stroke, telerehabilitation, upper limb
ABSTRAK
Latar Belakang: Stroke merupakan salah satu penyebab disabilitas jangka panjang, dengan gangguan fungsi motorik ekstremitas atas membatasi kemandirian fungsional serta kualitas hidup. Telerehabilitasi berbasis AI sebagai alternatif layanan rehabilitasi memungkinkan pemberian terapi secara intensif, spesifik terhadap tugas, dan terindividualisasi dari rumah. Namun, bukti mengenai efektivitas telerehabilitasi berbasis AI masih terbatas.
Metode: Pencarian literatur dilakukan pada PubMed, Embase, Scopus, Cochrane CENTRAL, dan Web of Science hingga Juni 2025. Studi uji acak terkontrol dan kuasi-eksperimental yang melibatkan pasien dewasa pascastroke dengan gangguan ekstremitas atas disertakan. Studi membandingkan telerehabilitasi berbasis AI dengan terapi konvensional yang dipandu terapis atau terapi jarak jauh standar, disertai luaran tervalidasi seperti Fugl-Meyer Assessment untuk Ekstremitas Atas (FMA-UE) dan Motor Activity Log (MAL). Penilaian risiko bias dilakukan menggunakan alat Cochrane, dan analisis meta dilakukan dengan model efek acak.
Hasil: Lima studi dengan total 339 partisipan memenuhi kriteria inklusi. Hasil meta-analisis menunjukkan tidak terdapat perbedaan bermakna secara statistik antara kelompok telerehabilitasi berbasis AI dan terapi konvensional. Perbedaan rerata gabungan FMA-UE adalah 0.55 (95% CI: -0.60 to 1.08; p=0.38; I²=89%), sedangkan untuk MAL sebesar 0.36 (95% CI: -0.45 to 1.17; p=0.39). Kedua kelompok menunjukkan perbaikan klinis bermakna, tanpa kejadian efek samping serius.
Kesimpulan: Telerehabilitasi berbasis AI merupakan alternatif yang layak untuk pemulihan fungsi ekstremitas atas pascastroke.
Kata kunci: kecerdasan buatan, pemulihan motorik, stroke, telerehabilitasi, ekstremitas atas
Article Details
How to Cite
Caesarani, F. P., Noviana, I., & Dwiwulandari, M. D. (2026). Comparative Effectiveness of AI-Assisted Telerehabilitation Versus Conventional Rehabilitation for Upper Limb Recovery After Stroke: A Systematic Review and Meta-Analysis. Indonesian Journal of Physical Medicine and Rehabilitation, 15(01), 33 - 42. https://doi.org/10.36803/indojpmr.v15i01.538
Section
Literature Review
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References
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4. Gittler M, Davis AM. Guidelines for adult stroke rehabilitation and recovery. JAMA. 2018;319(8):820.
5. Sirsat MS, Ferme E, Camara J. Machine learning for brain stroke: a review. J Stroke Cerebrovasc Dis. 2020;29(10):105162.
6. Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, Moura Neto E, et al. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on the ICF concept. Top Stroke Rehabil. 2022;29(5):331–346.
7. Mahmoud H, Aljaldi F, El-Fiky A, Battecha K, Thabet A, Alayat M, et al. Artificial intelligence machine learning and conventional physical therapy for upper limb outcome in patients with stroke: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2023;27(11):4812–4827.
8. Rahman S, Sarker S, Haque AKMN, Uttsha MM, Islam MF, Deb S. AI-driven stroke rehabilitation systems and assessment: a systematic review. IEEE Trans Neural Syst Rehabil Eng. 2023;31:192–207.
9. Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012;26(8):923–931.
10. El Naamani K, Musmar B, Gupta N, Ikhdour O, Abdelrazeq H, Ghanem M, et al. The artificial intelligence revolution in stroke care: a decade of scientific evidence in review. World Neurosurg. 2024;184:15–22.
11. Adams RJ, Ellington AL, Kuccera KA, Leaman H, Smithson C, Patrie JT. Telehealth-guided virtual reality for recovery of upper extremity function following stroke. OTJR (Thorofare N J). 2023;43(3):446–456.
12. Ballester BR, Nirme J, Camacho I, Duarte E, Rodriguez S, Cuxart A, et al. Domiciliary VR-based therapy for functional recovery and cortical reorganization after stroke: a randomized controlled trial. JMIR Serious Games. 2017;5(3):e15.
13. Adie K, Schofield C, Berrow M, Wingham J, Humfryes J, Pritchard C, et al. Does the use of Nintendo Wii Sports™ improve arm function after stroke? A randomized controlled trial and economic analysis. Clin Rehabil. 2017;31(2):173–185.
14. Standen P, Threapleton K, Richardson A, Connell L, Brown D, Battersby S, et al. A low-cost virtual reality system for home-based rehabilitation of the arm following stroke: a randomized controlled feasibility trial. Clin Rehabil. 2017;31(3):340–350.
15. Slijper A, Svensson KE, Backlund P, Engstrom H, Sunnerhagen KS. Computer game-based upper extremity training in the home environment in stroke persons: a single-subject design. J Neuroeng Rehabil. 2014;11:35.
16. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health. 2021;18(1):271.
17. Barry DT. Adaptation, artificial intelligence, and physical medicine and rehabilitation. PM R. 2018;10(9 Suppl 2):S215–S219.
18. Adikari A, Nawaratne R, De Silva D, Carey DL, Walsh A, Baum C, et al. Is mild really mild? Generating longitudinal profiles of stroke survivor impairment and impact using unsupervised machine learning. Appl Sci. 2024;14(15):6800.
19. Cramer SC, Dodakian L, Le V, McKenzie A, See J, Augsburger R, et al. Efficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke. JAMA Neurol. 2019;76(9):1079–1087.
20. Adikari A, Hernandez N, Alahakoon D, Rose ML, Pierce JE. From concept to practice: application of artificial intelligence to aphasia diagnosis and management. Disabil Rehabil. 2024;46(7):1288–97.
2. Vanhook P. The domains of stroke recovery. J Neurosci Nurs. 2009;41(1):6–17.
3. Dobkin BH. Rehabilitation after stroke. N Engl J Med. 2005;352(16):1677–1684.
4. Gittler M, Davis AM. Guidelines for adult stroke rehabilitation and recovery. JAMA. 2018;319(8):820.
5. Sirsat MS, Ferme E, Camara J. Machine learning for brain stroke: a review. J Stroke Cerebrovasc Dis. 2020;29(10):105162.
6. Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, Moura Neto E, et al. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on the ICF concept. Top Stroke Rehabil. 2022;29(5):331–346.
7. Mahmoud H, Aljaldi F, El-Fiky A, Battecha K, Thabet A, Alayat M, et al. Artificial intelligence machine learning and conventional physical therapy for upper limb outcome in patients with stroke: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2023;27(11):4812–4827.
8. Rahman S, Sarker S, Haque AKMN, Uttsha MM, Islam MF, Deb S. AI-driven stroke rehabilitation systems and assessment: a systematic review. IEEE Trans Neural Syst Rehabil Eng. 2023;31:192–207.
9. Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012;26(8):923–931.
10. El Naamani K, Musmar B, Gupta N, Ikhdour O, Abdelrazeq H, Ghanem M, et al. The artificial intelligence revolution in stroke care: a decade of scientific evidence in review. World Neurosurg. 2024;184:15–22.
11. Adams RJ, Ellington AL, Kuccera KA, Leaman H, Smithson C, Patrie JT. Telehealth-guided virtual reality for recovery of upper extremity function following stroke. OTJR (Thorofare N J). 2023;43(3):446–456.
12. Ballester BR, Nirme J, Camacho I, Duarte E, Rodriguez S, Cuxart A, et al. Domiciliary VR-based therapy for functional recovery and cortical reorganization after stroke: a randomized controlled trial. JMIR Serious Games. 2017;5(3):e15.
13. Adie K, Schofield C, Berrow M, Wingham J, Humfryes J, Pritchard C, et al. Does the use of Nintendo Wii Sports™ improve arm function after stroke? A randomized controlled trial and economic analysis. Clin Rehabil. 2017;31(2):173–185.
14. Standen P, Threapleton K, Richardson A, Connell L, Brown D, Battersby S, et al. A low-cost virtual reality system for home-based rehabilitation of the arm following stroke: a randomized controlled feasibility trial. Clin Rehabil. 2017;31(3):340–350.
15. Slijper A, Svensson KE, Backlund P, Engstrom H, Sunnerhagen KS. Computer game-based upper extremity training in the home environment in stroke persons: a single-subject design. J Neuroeng Rehabil. 2014;11:35.
16. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health. 2021;18(1):271.
17. Barry DT. Adaptation, artificial intelligence, and physical medicine and rehabilitation. PM R. 2018;10(9 Suppl 2):S215–S219.
18. Adikari A, Nawaratne R, De Silva D, Carey DL, Walsh A, Baum C, et al. Is mild really mild? Generating longitudinal profiles of stroke survivor impairment and impact using unsupervised machine learning. Appl Sci. 2024;14(15):6800.
19. Cramer SC, Dodakian L, Le V, McKenzie A, See J, Augsburger R, et al. Efficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke. JAMA Neurol. 2019;76(9):1079–1087.
20. Adikari A, Hernandez N, Alahakoon D, Rose ML, Pierce JE. From concept to practice: application of artificial intelligence to aphasia diagnosis and management. Disabil Rehabil. 2024;46(7):1288–97.