## 5 Conclusions  
Modern technologies can profoundly change the process of patient recovery. After reviewing developments and advancements in the field of scientific rehabilitation, it has become clear that the combination of these technologies could make rehabilitation more accessible and personalized. These technologies help improve the accuracy of diagnoses, predict the speed of recovery, and adapt treatment programs based on the individual needs of each patient. However, there are still challenges that need to be addressed to improve and scale these systems, such as the incompatibility of frameworks and structures, as well as issues of patient record protection and privacy. This review analyzed 68 studies--of which 11 were devoted to the relevance of developing integrated medical platforms for rehabilitation. A breakdown of the other articles includes: 6 on medical platforms for rehabilitation; 18 on the role of the IoT in medical monitoring; 15 on the use of ML in rehabilitation systems; 12 on virtual assistants and chatbots that help patients; 3 on the real-world challenges and barriers of using LLMs; and 3 on the ethical and regulatory challenges of LLMs. In our review article, we examined medical rehabilitation systems in detail, focusing on the use of AI, the IoT, wearable devices, and virtual assistants that support patients at all stages of recovery. These technologies significantly transform rehabilitation methods, providing personalized and accessible solutions for the treatment of various conditions. AI and machine learning (ML) technologies are actively used to personalize rehabilitation and predict and adjust the patient's recovery plan. These technologies allow for the processing of vast volumes of statistical data obtained from patients to analyze their physical and psycho-emotional state. The ability to quickly respond to changes in the patient's condition is essential for people recovering from strokes, surgeries, or injuries. AI-driven virtual reality (VR) has the potential to simulate real-life tasks, which helps accelerate patient recovery. It allows patients to visualize their progress and receive precise feedback at specific stages of rehabilitation. The use of virtual assistants based on LLMs plays a key role in rehabilitation. Chatbots based entirely on LLMs provide 24 h support to patients, help them adapt to rehabilitation programs, check the effectiveness of exercises, and offer motivating tips. These technologies are actively used to provide psycho-emotional support to patients, especially those undergoing long-term rehabilitation. In addition, chatbots can examine and classify data obtained from patients, for example, from surveys, which allows them to more accurately predict rehabilitation outcomes. Nevertheless, despite this impressive progress, digital rehabilitation remains in an early stage of clinical translation and faces several interrelated challenges. More than half of those in need of rehabilitation in low- and middle-income countries still do not receive adequate care. Clinical approaches remain resource-intensive and costly, while informal caregivers--according to a survey conducted in East Kazakhstan--are often undertrained and overburdened. Patient engagement is undermined by low motivation, fatigue, and a lack of clear instructions. While gamification and visual feedback help address this, their effectiveness is limited by the lack of compatibility between various hardware and software solutions, as well as the absence of unified data exchange standards across devices, platforms, and hospital information systems. The continuous flow of sensitive physiological data raises critical issues related to privacy, encryption, patient consent, and cross-border data transfer. This requires robust cybersecurity mechanisms and the implementation of privacy-preserving analytical approaches, such as federated learning. Invasive devices require surgical implantation and costly maintenance, while non-invasive devices are vulnerable to noise, signal loss, and a limited battery life.