Volume 30, Issue 171, May 2026

From Algorithms to Patient Care: Artificial Intelligence in Neurological Rehabilitation – A Review

Maja Kondratowicz^♦, Aleksandra Figzał², Kamila Kałamarz², Kinga Żmuda³, Maciej Świerczyna⁴, Maja Czerniachowska⁵, Marcin Kaniewski¹, Martyna Wojnowska⁶, Wiktoria Polkowska⁷, Michał Grabek²

¹The Independent Public Hospital No. 4, Doktora Kazimierza Jaczewskiego 8, 20-954 Lublin, Poland
²Karol Marcinkowski University Hospital, Zyty 26, 65-046 Zielona Góra, Poland
³University Clinical Hospital of Opole, al.W.Witosa 26 45-401 Opole, Poland
⁴Ministry of the Interior and Administration Hospital, Północna 42, 91-425 Łódź, Poland
⁵Medical University of Łódź, al. Kościuszki 4, 90-419 Łódź, Poland
⁶Mikolaj Pirogov Provincial Specialist Hospital, Wólczańska 191/195, 90-001 Łódź, Poland
⁷Central Clinical Hospital, Medical University of Łódź, Medical University of Łódź, Pomorska 251, 90-213 Łódź, Poland

^♦Corresponding author
Maja Kondratowicz, The Independent Public Hospital No. 4, Lublin, Poland

ABSTRACT

Stroke, traumatic brain injury, and multiple sclerosis are neurological disorders that cause a lot of long-term disability and put a lot of strain on society and the economy. Rehabilitation is necessary for regaining functionality. But standard practices often don't allow for much personalization, intensity, or accessibility. Artificial intelligence (AI) and machine learning (ML) have recently emerged as promising instruments for enhancing neurological rehabilitation by facilitating more accurate evaluation, tailored therapy planning, and continuous monitoring of patient progress. The goal of this review is to give an overview of how AI is currently being used in neurological rehabilitation. It includes things like telerehabilitation, brain-computer interfaces, robotic-assisted therapy, and motor recovery after a stroke. The research demonstrates that AI-driven interventions can enhance therapy intensity, forecast recovery outcomes, and utilize objective assessments of motor and cognitive function. Telerehabilitation platforms make it easier to get care outside of the clinic. Robotic systems and AI-enhanced virtual environments, on the other hand, allow patients to train in ways customized to their needs. Even with these changes, there are still problems with algorithm transparency, data privacy, and guaranteeing fair access for everyone, including in clinical practice. Our goal in this review is to demonstrate how AI and ML can revolutionize neurological rehabilitation by providing scalable, individualized approaches that improve clinical results. Long-lasting effectiveness, standardization procedures, and useful workflow integration should be the main topics of future research.

Keywords: artificial intelligence, machine learning, neurological rehabilitation, stroke recovery, robotic therapy, brain-computer interface, tele-rehabilitationartificial intelligence, machine learning, neurological rehabilitation, stroke recovery, robotic therapy, brain-computer interface, tele-rehabilitation

Medical Science, 2026, 30, e87ms3833

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Published: 15 May 2026