A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring

Abstract

Artificial intelligence (AI) and machine learning (ML) have introduced new approaches to medical rehabilitation. These technological advances facilitate the development of large-scale adaptive rehabilitation platforms that can be tailored to individual patients. This review focuses on key technologies, including AI-driven rehabilitation planning, IoT-based patient monitoring, and Large Language Model (LLM)-powered virtual assistants for patient support. This review analyzes existing systems and examines how technologies can be combined to create comprehensive rehabilitation platforms that provide personalized care. For this purpose, a targeted literature search was conducted across leading scientific databases, including Scopus, Google Scholar, and IEEE Xplore. This process resulted in the selection of key peer-reviewed articles published between 2018 and 2025 for a detailed analysis. These studies highlight the latest trends and developments in medical rehabilitation, showcasing how digital technologies can transform rehabilitation processes and support patients. This review illustrates that AI, the IoT, and LLM-based virtual assistants hold significant promise for addressing current healthcare challenges through their ability to enhance, personalize, and streamline patient care.

1. Introduction

According to estimates, 2.4 billion people worldwide are in need of rehabilitation, but in some low- and middle-income countries, more than 50% of them lack access to necessary services. This gap is particularly exacerbated by emergencies such as wars and natural disasters [1]. Currently, over 800 million people suffer from chronic diseases, and a significant portion do not have ready access to medical centers, which complicates their recovery [2]. There is a clear need to build effective medical rehabilitation systems. An integrated framework is highly pertinent, especially for post-hospital discharge care when patients often feel uncertain about their home rehabilitation regimen. Conventional approaches to rehabilitation are typically resource-intensive, making them difficult to implement on a large scale [3]. Digital technologies, in this regard, offer solutions that can make treatments more flexible and accessible. Medical rehabilitation systems that allow remote interactions between doctors, devices, and patients are paramount for post-hospital discharge care, especially when physical contact is restricted. The challenges facing medical rehabilitation organizations today highlight the need for further the optimization and enhancement of current approaches. A recent systematic review [4] emphasizes the growing interest in combining robot-assisted technologies with electrical stimulation for lower extremity rehabilitation in stroke patients, highlighting not only the potential functional improvements but also the current limitations in comparability due to heterogeneous outcome measures and intervention protocols. Furthermore, a study in the East Kazakhstan region revealed that both caregivers and patients face significant challenges in home care, including insufficient training in rehabilitation procedures and limited resources [5].

Digital technologies can help therapists remotely monitor patient performance, provide advice, and adjust treatment plans [3]. Patient engagement is crucial, as low motivation, fatigue, and a lack of clear instructions can reduce the adherence to rehabilitation programs [4]. The nuanced nature of the recovery process, especially during self-rehabilitation at home, often presents difficulties. Implementing medical rehabilitation systems that integrate wearable sensors and AI can overcome these gaps by personalizing care, improving monitoring, increasing patient participation, and reducing the burden on healthcare facilities [3]. The use of AI and machine learning to analyze patient movements, assess exercise accuracy, and detect problems makes rehabilitation more precise and effective, which is particularly important during post-surgical rehabilitation [6].

In this regard, telerehabilitation allows healthcare professionals to actively monitor the recovery process, while patients can observe their own progress, thereby increasing motivation and awareness [2]. A crucial aspect is continuous monitoring through wearable sensors, such as IMUs, which track patient movements and physiological parameters at home and transmit this data for analysis. This process enables healthcare providers to promptly adjust rehabilitation plans based on the patient’s real-time condition. Such adaptability significantly increases the efficiency of the recovery process by personalizing treatment based on data from wearable devices [2,7]. In the field of assistive devices, for example, the development and testing of a personalized ankle exoskeleton demonstrated the effectiveness of individualized rehabilitation technologies [8].

Currently, gamified rehabilitation is gaining popularity, as it allows patients to perform exercises in a serious game format using webcams and deep learning algorithms to track movements, eliminating the need for specialized equipment [3]. The studies show that wearable devices, mobile applications, telerehabilitation platforms, virtual reality, and exergaming contribute to maintaining physical activity and improving the quality of life [9]. Elements of virtual reality and serious games make the rehabilitation process more engaging and entertaining [3]. This type of monitoring allows for the early detection of deviations in exercise performance or the patient’s condition, which can prevent injuries and complications [6]. By reducing the number of visits to a clinic, the burden on medical institutions and the resources required from patients are also diminished [10].

While these individual technologies are powerful, their true potential is unlocked when they are integrated into a single, cohesive system. The convergence of these technologies is leading to the creation of holistic AI-assisted platforms that represent a new paradigm for personalized, data-driven care. A conceptual model for such a platform is illustrated in Figure 1.

The architecture in Figure 1 is built on the synergy of three key technological pillars: sensor-based Internet of Things (IoT) devices, machine learning (ML), and Large Language Models (LLMs). IoT devices, such as EMG sensors, address the challenge of remote monitoring by continuously collecting objective physiological and behavioral data. This data is then fed into machine learning algorithms, which form the core of the intelligent model, to assess progress, detect anomalies, and personalize the rehabilitation plan in real-time. Finally, Large Language Models provide an intuitive interface for patient interaction, solving the issues of a low engagement and lack of clear instructions by delivering personalized feedback, motivation, and support through virtual assistants or chatbots. This integrated feedback loop creates a flexible, scalable, and remotely controlled rehabilitation environment capable of empowering patients and reducing the burden on caregivers and the healthcare system.

The goal of this review article is to explore innovative medical rehabilitation systems with a focus on the use of AI, ML, and sensor technologies to create scalable platforms. This review presents an analysis of existing technologies, including personalized recovery through AI, the use of the IoT for patient monitoring, and the role of virtual assistants and chatbots powered by Large Language Models in patient support, and methods of integrating various technologies to create effective rehabilitation platforms that can adapt to the individual needs of patients in real-time.

2. Materials and Methods

This study was conducted as a narrative review to synthesize the current research on the integration of modern digital technologies into medical rehabilitation and patient health monitoring. This review focuses on studies involving IoT systems, wearable sensor technologies, AI, and mobile health applications within rehabilitation contexts, investigating digital innovations aimed at improving therapeutic efficiency, enabling remote monitoring, and promoting individualized strategies. As a narrative review, this paper does not employ formal data extraction procedures or systematic assessments of the quality or risk of bias of the included studies. While this may limit quantitative comparisons, it enables a broader and more exploratory examination of interdisciplinary developments. The relevant literature was identified through a structured search of major academic platforms, including Scopus, Google Scholar, IEEE Xplore, MDPI, Elsevier (via ScienceDirect), Springer, PubMed, JMIR, ACM Digital Library, BMC, Lippincott, and the official database of the World Health Organization (WHO). The identification of relevant academic literature was carried out using precise search terms, including “medical rehabilitation”, “Internet of Things in healthcare”, “wearable sensor technologies”, “artificial intelligence in patient care”, “remote monitoring systems”, and “smart digital health”. These scientific publications were examined in detail, having been selected from an initial pool of approximately 180 academic sources. A total of 112 scientific publications were chosen in accordance with clear inclusion criteria designed to define the scope of this narrative review. These criteria included the thematic relevance to rehabilitation, evidence of either the use of advanced AI and ML algorithms for real-time monitoring, integration of novel sensor-based systems, development of platforms with a significantly improved personalization or adaptability compared to standard systems, publication within the 2017–2025 timeframe, and indexing in internationally recognized scholarly databases. The selected works form the basis for the thematic discussion in this review, illustrating key trends in the digital transformation of rehabilitation. An analysis of the publications revealed that most research originated from scholars in the United States, Italy, and China, underscoring a growing global interest in developing intelligent systems for patient monitoring and therapeutic support.

Figure 2 illustrates the annual distribution of the 112 publications included in this review, highlighting a clear upward trend in the research activity from 2017 to 2025. The horizontal axis represents publication years, while the vertical axis denotes the number of publications recorded each year. Starting with a single publication in 2017, the number increased to 7 in 2018 before declining to 3 in 2019. A renewed rise occurred in 2020 with 11 papers, followed by a slight decrease and stabilization at 9 publications in both 2021 and 2022. The field experienced notable growth in 2023, reaching 16 papers, and peaked sharply in 2024 with 33 publications. This surge coincides with the broader availability of transformer-based LLM platforms and the maturation of IoT infrastructures capable of continuous patient monitoring. In 2025, the count declined slightly to 23 papers but remained significantly higher than in previous years, indicating a potential shift from exploratory development to more mature stages, including clinical validation and implementation.

An analysis of the geographical distribution of the 112 publications included in this review (2017–2025) is presented in Figure 3. The chart displays the number of papers per country based on the affiliation of the authors. The largest contribution comes from the “Others” category, which aggregates countries with one to three publications each and accounts for approximately sixty-seven papers, more than half of the total dataset. Among the individually listed countries, the United States ranks first with 20 publications (17.9%), followed by Italy with 12 publications (10.7%) and China with 9 (8%). The United Kingdom contributed 4 publications, making it the least represented among the specified countries. The analysis indicates a growing research focus in these leading countries on integrating artificial intelligence into rehabilitation systems.

The literature selection process involved sequential filtering at each stage, as illustrated in the PRISMA diagram in Figure 4. An initial search across authoritative scientific databases (including PubMed, Scopus, IEEE Xplore, Web of Science, and Springer) for the period of 2017–2025 yielded 180 publications. These publications spanned four key thematic areas: digital rehabilitation platforms, IoT sensor technologies for monitoring, AI/ML applications in rehabilitation, and LLM-powered virtual assistants for patient support. After the automatic removal of 12 duplicates, 168 unique records were retained for screening. At this stage, titles and abstracts were screened for thematic relevance, leading to the exclusion of 28 publications.

Consequently, 140 articles were selected for full-text retrieval. Of these, full texts could not be accessed for six articles. The remaining 134 publications underwent a full-text evaluation against the inclusion criteria. Based on this evaluation, another 22 articles were excluded for the following reasons: out of scope (n = 13), insufficient methodological detail (n = 6), and unsupported language (n = 3). This process resulted in a final selection of 112 scientific publications for inclusion in this review.

3. Results

3.1. Medical Platforms for Rehabilitation

The new generation of rehabilitation platforms is exemplified by wearable, ECG/EMG-driven systems that combine real-time physiological monitoring with a robotic glove. This platform enables closed-loop upper-limb training, in which SVM-based arousal and fatigue detection algorithms, along with adaptive controls, dynamically adjust exercise intensity while clinicians view metrics such as heart rate, beat-to-beat intervals, and muscle activation on an interactive dashboard [11].

Currently, medical platforms such as I-TROPHYTS are widely used for remote rehabilitation monitoring, as they offer personalized rehabilitation plans based on patient historical data [12]. These systems integrate IoT devices to collect and analyze movement data and monitor the patient’s condition. A unique feature of the I-TROPHYTS platform is its AI-powered humanoid robot, which assists in the therapy by tracking and analyzing the patient’s movements in real-time to automatically adjust the therapeutic process. The robot also demonstrates exercises and adapts the training program based on the patient’s condition. This principle of real-time data analysis also applies to more focused contexts; for example, postoperative knee monitoring systems have demonstrated that edge computing can effectively track knee flexion and extension angles [13].

To illustrate the evolution of such solutions,

Table 1. Comparative overview of medical rehabilitation platforms using AI, IoT, and Robotics.

Platform/Source	Architecture	Key Technologies	Purpose	Personalization (Qualitative)	Evidence Strength	Clinical Maturity	Cost-Effectiveness	Reference
REHOME	IoT + local interfaces	sensors, cognitive games, VR	comprehensive cognitive and motor telerehabilitation	patient data use: 100%, adaptation: personalized	Pilot studies, n = 28–27–15; SUS > 68	Advanced; multi-domain remote rehab in hospitals	Likely cost savings via reduced hospital use	[14]
ReMoVES	IoMT + remote	telemedicine, IoT	rehabilitation for multiple sclerosis	patient data use: 100%, adaptation: weekly	Feasibility study, n = 2	Rehab for MS; positive user engagement	Potential savings from remote sessions	[13]
I-TROPHYTS	IoT + humanoid robotics	wearable IoT, sensors, AI, robotics	autonomous monitoring and physiotherapy	patient data use: 100% adaptation: real-time	Small trial, n = 6; ~100% accuracy	Partial; home rehab via AI + robot	Scalable; one therapist for many patients	[12]
3D-printing + AI	computer-aided design (CAD) + cloud system	AI optimization, bioscanning	custom prosthetics and orthotics	patient data use: 100%, adaptation: personalized	Conceptual; no clinical trial	Limited; wrist rehab prototype	Possible savings via 3D-printing	[15]
AI + CT Diagnostics	cloud-based	ResNet50, segmentation, AI	diagnostics/monitoring	patient data use: 100%, adaptation: real-time	No data available	No data available	No data available	[12]
Pheno4U Platform	cloud-based + Mobile App	motion sensors, data tracking, integration with hospital Information system (HIS)	remote monitoring and rehabilitation	patient data use: 100%, adaptation: weekly	Clinical study, n = 98; p < 0.001	TKA aftercare with sensors/app; well-accepted	Supports autonomy; reduces inpatient needs	[16]
Haodf (ind./comm. model)	online service platform	multilevel interaction	virtual consultations	patient data use: 100%, adaptation: continuous	No data available	No data available	No data available	[13]

provides a brief comparison of the most representative rehabilitation platforms in the field. The table outlines their system architectures, key technologies, primary therapeutic goals, and, importantly, the degree of personalization for each platform. REHOME and ReMoVES, presented in Table 1, are platforms designed for the rehabilitation of patients with neurological disorders [14,15]. These platforms provide personalized rehabilitation support for patients with multiple sclerosis and Parkinson’s disease using motor and cognitive approaches. REHOME offers exercises to improve memory and concentration using virtual reality [15], while ReMoVES uses Internet of Medical Things (IoMT) devices for remote monitoring and the creation of personalized rehabilitation plans [15]. A mobile application, ForPatientApp, was also integrated for remote monitoring, allowing both patients and doctors to easily track the range of motion (ROM) and monitor health conditions after surgeries, such as total knee arthroplasty [16].

The comparative indicators in Table 1 show that these platforms differ not only in their technological stack but also in their capacity for personalization—ranging from periodic adaptations to fully personalized, real-time adjustments. Moreover, the platforms vary significantly in their level of evidence, from small-scale feasibility studies to larger clinical trials, indicating different stages of scientific validation. Their clinical maturity also spans a wide spectrum, from conceptual prototypes to fully deployed systems used in hospital settings. From an economic perspective, these platforms achieve cost-effectiveness through strategies such as reducing hospitalizations, enabling remote monitoring, and promoting patient autonomy. This analysis of medical platforms reveals that the rapid convergence of artificial intelligence, advanced robotics, and comprehensive IoT sensing represents a highly promising and innovative direction.

3.2. Role of IoT Sensors in Rehabilitation

Rehabilitation science has significantly evolved since healthcare professionals used a stopwatch to measure a patient’s walking time and a plastic goniometer to assess joint angles [17]. The demand from therapists for higher training intensity and more sensitive feedback led to the emergence of small, affordable sensors embedded in splints, shoes, and even coffee cups. Today, their impact is so profound that modern motor rehabilitation programs increasingly integrate sensor-based systems, as shown in the analysis presented in Table 2. These sensors quantitatively assess physiological parameters, process data in real-time, and instantly convert it into mechanical or visual feedback that helps restore mobility.

Electrocardiography (ECG) remains the gold standard for assessing the heart’s electrical activity. Whether using classical Ag/AgCl wet electrodes or modern dry-textile variants, these sensors deliver millivolt precision, enabling the robust calculation of the heart rate and heart-rate variability—metrics indispensable for calibrating exercise intensity in cardiac rehabilitation [18]. Advanced systems now incorporate edge computing to perform initial signal processing—thus reducing the transmission latency and power consumption—and support personalized adaptive protocols in environments such as home-based telerehabilitation [19]. While ECG excels at beat-to-beat accuracy, photoplethysmography (PPG) extends monitoring to everyday contexts. Cost-effectively integrated into smartwatches and rings, PPG sensors track peripheral blood volume changes to provide continuous heart rate and SpO₂ readouts. However, during dynamic movement, the optical signal remains vulnerable to motion artifacts and ambient light interference, which can reduce its accuracy compared to ECG, especially under physically active conditions [20].

Since cardiovascular metrics alone are insufficient for guiding muscular retraining, the attention also shifts from blood flow to muscle activation. While invasive needle EMG offers an unmatched spatial resolution within deep tissues, non-invasive surface EMG (sEMG) utilizes electrodes placed on the skin over target muscles. The surface variant streams real-time coordination and fatigue data to wearable IoT hubs, giving therapists immediate feedback during strengthening routines [21]. The integration of radar technologies into BCG monitoring helps overcome usability challenges encountered with conventional contact devices, and it supports unobtrusive long-term monitoring in both clinical and home environments [22].

When electrical fidelity is required but direct contact is impractical, a capacitive ECG (cECG) offers a viable alternative. By using electrodes separated from the skin by a dielectric layer, a cECG enables long-term monitoring without gels or adhesives. However, variations in the electrode-to-skin distance and body movement may introduce noise, requiring appropriate signal processing to maintain reliability [23]. If physical contact must be avoided, Ultra-Wideband (UWB) radar provides a contactless electromagnetic solution. It emits short pulses across a broad frequency band to detect subtle displacements and spatial positioning through clothing and even walls. This enables the high-resolution, continuous monitoring of posture, movement, and vital signs without requiring any wearable devices. UWB radar supports applications such as fall detection, gait analysis, and postural monitoring, though it relies on precise signal processing due to environmental interference [24,25]. Its extremely low transmission power and ability to operate unobtrusively within a living environment enhance patient comfort and broaden IoT applications in smart rehabilitation systems, though the complexity of signal processing to mitigate multipath interference remains a technical challenge [24].

Physiological readiness often dictates a patient’s potential for recovery. As demonstrated in a study at a heart center, researchers used non-invasive impedance cardiography during routine stress tests to translate beat-to-beat signals into a contractility index. A full curve on this index indicated an eightfold greater chance of a significant VO₂ improvement with exercise-based therapy, prompting clinicians to proceed, while a diminished curve led them to modify the treatment protocol. This unseen sensing transformed the treatment plan without new wires or extra appointments [26].

Movement, traditionally measured by eye or in specialized labs, can now be quantified in everyday settings. While early inertial modules were met with skepticism, contemporary research shows their effectiveness. For example, following a total knee arthroplasty, a two-sensor system on the thigh and shank monitored flexion with less than three degrees of discrepancy compared to a Vicon motion lab during walking and stair climbing [27]. These same sensor pairs achieved an accuracy within eight degrees for the Timed-Up and Go test, allowing patients to demonstrate improvement remotely [28]. The structurally complex shoulder has also been effectively tracked using three sensors (on the sternum, upper arm, and wrist) to provide real-time feedback on a smartphone display. Patients with adhesive capsulitis who used this augmented feedback regained more range of motion and reported less disability compared to those using only printed instructions [29]. In this context, the sensor acts not as a silent witness but as a coach, counting repetitions only when a target angle is achieved.

Timely medical intervention is particularly critical during rehabilitation after strokes, surgeries, or neurological treatments, as early adjustments often determine the overall outcome. For instance, one study describes the development of an exoskeleton for individuals with physical impairments, designed for mobility in smart-city environments. It features automatic limb control based on an IoT platform and an AI-powered navigation module that employs an IoT-supported SLAM method [30]. Another innovation is a 3D-printed, bilateral hand exoskeleton made of biodegradable plastic, which operates with five miniature IMUs embedded in a glove on the healthy hand. The mirrored motion data is transmitted via Wi-Fi to servo motors that flex the fingers of the paretic hand in real-time. Costing little, the system transforms the patient’s stronger limb into an intuitive joystick for the paretic one, supporting rehabilitation by encouraging symmetric movement and preventing disuse [31].

Adherence is also linked to comfort. To this end, engineers are integrating sensors directly onto the skin. One such device is a flexible copper/polyimide (Cu/PI) serpentine trace printed on breathable silicone, which sits so lightly on the forearm that stroke patients forget they are wearing an eight-channel EMG array [32]. This patch classified seven hand gestures with a 95.81% accuracy and recorded muscle activity during robotic hand training. For direct robot control, six specific gesture classes were used, based on electromyography signals gathered from forearm muscles to operate a mobile robot with a gripping mechanism aimed at restoring fine motor skills. With a high gesture recognition accuracy supported by Gaussian Mixture Networks, this system provides engaging, targeted training suitable for both clinical and home use [33]. Efforts to support stroke recovery have led to the clinical evaluation of depth sensors, like the Xbox One Kinect, aimed at tracking upper limb movements. Although this method shows promise, researchers have noted considerable limitations in tracking joint positions—particularly at the shoulder, elbow, and wrist—calling for improvements that incorporate temporal data and anatomical models to better handle challenges like occlusion [34]. To further enhance stroke rehabilitation, an advanced rehabilitation system has been developed using wearable electromyography smart sensors integrated with functional electrical stimulation and virtual reality, offering a personalized and engaging rehabilitation experience for stroke survivors. By incorporating these sensors in a closed-loop system, the platform supports both hospital- and community-based rehabilitation stages, enhancing accessibility and effectiveness [35].

Sensors have even moved beyond the body, penetrating the spaces where people rest and recover. The thick Emfit QS bed sensor, based on ballistocardiography (BCG), is placed under a rehabilitation mattress in the chest area of a sleeping patient and successfully detects the heartbeat, breathing patterns, and small movements of the torso at night [36]. Harnessing the potential of wearable technology, another approach combines accelerometers and Bluetooth Low-Energy beacons to monitor both the activity levels and location among elderly patients in subacute rehabilitation. This setup enables clinicians to identify subtle indicators, such as the percentage of time spent standing or lying down, offering valuable insights for predicting outcomes like hospital readmission [37]. At the cognitive level, deep learning speech recognizers embedded in virtual reality environments guide elderly people through language exercises, supporting therapy in rural clinics. While the current system lacks speech synthesis and real-time interaction, future research aims to enable a human–machine dialog for more realistic and engaging training scenarios [38].

Nanomaterial-based biosensors hold tremendous potential, especially when integrated with cloud-based AI technologies [39]. However, the authors also emphasize several engineering challenges, including data heterogeneity, bandwidth limitations, and privacy concerns. Another study explores the growing relevance of wearable IoT devices within structured rehabilitation programs, which the enable real-time tracking of physical activity and heart rates, allowing clinicians to access patient data remotely [40]. In addition to improving motor function, ensuring an autonomous power supply has become a critical requirement for the long-term use of wearable rehabilitation technologies. Study [41] focuses on electromagnetic energy harvesting as a promising solution, while the broader review discusses additional mechanisms such as piezoelectric, triboelectric, and thermoelectric approaches. These devices process data locally on the sensor, eliminating the need for continuous wireless communication, making them especially suitable for long-term recovery scenarios with minimal maintenance requirements. Wireless sensor network simulations have also shown that reinforcement learning algorithms can determine which node sleeps, transmits, or forwards signals, extending battery life without sacrificing coverage [42].

Table 2. Overview of wearable and ambient IoT systems in medical rehabilitation.

IoT Device	Primary Parameters	Specific Application	References
Signal-morphology Impedance Cardiography (thoracic surface electrodes)	Beat-to-beat stroke volume, cardiac output, HR	Predict responsiveness to exercise-based cardiac rehab	[26]
MotionSense™ triaxial IMU (accel-gyro-mag) worn on thigh and shank	Knee flexion/extension angles, gait spatiotemporal metrics	Objective monitoring after total-knee arthroplasty	[27]
Wearable Magnetic-IMU (MIMU) strapped to tibia	3-D knee kinematics (flexion, varus/valgus, rotation)	Home-based knee rehab, validated vs. optoelectronic system	[28]
Wearable single-strap IMU on upper arm	Shoulder ROM, repetition quality	Home exercises for adhesive capsulitis (frozen-shoulder)	[29]
On-board IMU in 3D-printed hand exoskeleton + wireless MCU	Finger joint orientation and motion	Bilateral-mode hand exoskeleton control for stroke rehab	[31]
Conformal, stretchable, wireless epidermal sEMG array	Forearm muscle activity patterns	Hand-gesture recognition and stroke-hand functional training	[32]
EMG sensors	Gesture recognition, forearm muscle signals, mobile robot control	Restoring fine motor skills, interactive training for clinical and home use	[33]
Xbox One Kinect	Depth sensor-based pose estimation, joint position tracking	Stroke recovery, tracking upper limb movements	[34]
Wearable EMG smart sensors	Muscle activation signals (EMG data), movement response	Targeted stroke rehab via a closed-loop EMG system with Functional Electrical Stimulation (FES) and VR (Virtual Reality), used in hospital and home settings	[35]
Under-mattress ballistocardiography/pressure strip	Sleep stages, HR, movement counts	Overnight sleep monitoring during inpatient rehab	[36]
Wearable accelerometers, Bluetooth Low-Energy beacons	Activity levels, location tracking, time spent standing/lying down	Monitoring elderly patients in subacute rehabilitation, predicting hospital readmission outcomes	[37]
Microphone array + ambient sensor nodes	Speech acoustics, pronunciation metrics	Elderly speech-rehab learning assistance platform	[38]
Wireless Sensor Network (WSN) nodes integrating inertial and energy-monitoring chips	Limb kinematics + node energy status	Bilateral-mode hand exoskeleton control for stroke rehab	[42]

Table 2. Overview of wearable and ambient IoT systems in medical rehabilitation.

IoT Device	Primary Parameters	Specific Application	References
Signal-morphology Impedance Cardiography (thoracic surface electrodes)	Beat-to-beat stroke volume, cardiac output, HR	Predict responsiveness to exercise-based cardiac rehab	[26]
MotionSense™ triaxial IMU (accel-gyro-mag) worn on thigh and shank	Knee flexion/extension angles, gait spatiotemporal metrics	Objective monitoring after total-knee arthroplasty	[27]
Wearable Magnetic-IMU (MIMU) strapped to tibia	3-D knee kinematics (flexion, varus/valgus, rotation)	Home-based knee rehab, validated vs. optoelectronic system	[28]
Wearable single-strap IMU on upper arm	Shoulder ROM, repetition quality	Home exercises for adhesive capsulitis (frozen-shoulder)	[29]
On-board IMU in 3D-printed hand exoskeleton + wireless MCU	Finger joint orientation and motion	Bilateral-mode hand exoskeleton control for stroke rehab	[31]
Conformal, stretchable, wireless epidermal sEMG array	Forearm muscle activity patterns	Hand-gesture recognition and stroke-hand functional training	[32]
EMG sensors	Gesture recognition, forearm muscle signals, mobile robot control	Restoring fine motor skills, interactive training for clinical and home use	[33]
Xbox One Kinect	Depth sensor-based pose estimation, joint position tracking	Stroke recovery, tracking upper limb movements	[34]
Wearable EMG smart sensors	Muscle activation signals (EMG data), movement response	Targeted stroke rehab via a closed-loop EMG system with Functional Electrical Stimulation (FES) and VR (Virtual Reality), used in hospital and home settings	[35]
Under-mattress ballistocardiography/pressure strip	Sleep stages, HR, movement counts	Overnight sleep monitoring during inpatient rehab	[36]
Wearable accelerometers, Bluetooth Low-Energy beacons	Activity levels, location tracking, time spent standing/lying down	Monitoring elderly patients in subacute rehabilitation, predicting hospital readmission outcomes	[37]
Microphone array + ambient sensor nodes	Speech acoustics, pronunciation metrics	Elderly speech-rehab learning assistance platform	[38]
Wireless Sensor Network (WSN) nodes integrating inertial and energy-monitoring chips	Limb kinematics + node energy status	Bilateral-mode hand exoskeleton control for stroke rehab	[42]

3.3. AI and ML for Rehabilitation Systems

3.3.1. AI Algorithms and Machine Learning for Personalized Rehabilitation and Recovery Prediction

The continued prevalence of neurological and musculoskeletal disorders has highlighted the urgent need for innovative and effective rehabilitation methods. Traditional approaches often lack personalization, objective progress assessments, and the necessary tools to maintain patient motivation in the long term. To address these issues, the field of rehabilitation technology is rapidly developing, with ML and VR playing a key role. One area where these technologies have proven particularly useful is stroke rehabilitation, as restoring patients’ physical and cognitive functions requires precise, adaptive, and scalable solutions. Existing machine learning models show potential in rehabilitation but suffer from their scalability issues and limited ability to adapt to dynamic rehabilitation data, as they underutilize performance metrics such as completion and error rates, resulting in generalized rather than personalized treatment plans [43]. In response to these limitations, an optimized Bi-LSTM model using Firefly Optimization (FFO) was proposed. The proposed optimized FFO-Bi-LSTM model improves the accuracy, speed, and adaptivity by using Firefly Optimization to tune hyperparameters such as the learning rate and batch size, which is critical for real-time rehabilitation. FFO helps the model generalize to different patient states after a stroke. A statistical analysis of rehabilitation performance was conducted to compare deep learning and machine learning methods, as shown in Table 3 and Table 4. The analysis presents critical performance metrics, including the accuracy, recall, precision, and F1-score, providing a clear evaluation of their effectiveness. These results help establish the strengths and limitations of different predictive models within the rehabilitation domain, confirming the superior performance of the optimized FFO-Bi-LSTM model, which achieved a 99.06% accuracy, as indicated in Table 3. VR complements this approach by simulating real-world tasks in a controlled environment and providing data on the patient’s motor skills, cognitive functions, and physical responses, allowing FFO-Bi-LSTM to dynamically adapt the intensity of the therapy [44].

Table 3. Comparative performance analysis of multiple deep learning methods used in rehabilitation.

Methods	Accuracy	Recall	Precision	F1-Score	Training/Validation	Dataset Composition	Provenance/Accessibility	Reference
Proposed FFO-BI-LSTM	99.06	99.65	99	99.20	Not reported	Only illustrative values are shown	Private/Institutional (Real-time VR rehabilitation sessions collected by the authors’ lab)	[44]
Deep Neural Network (DNN)	92.50%	-	-	-	58 patients with shoulder diseases (80/20)	Age: 60.5 ± 9.7 (37–82); gender: 46.6% M, 53.4% F; conditions: adhesive capsulitis, rotator cuff disease; severity: The Shoulder Pain and Disability Index (SPADI) 43.9 ± 22.7 (0–100).	Private/Institutional (IMU-sensor data recorded at Seoul Metropolitan Government Boramae Medical Center, South Korea)	[45]
Artificial Neural Network (ANN)	86.4%	71.2%	-	-	128 subjects (one IRP per person); training (80.4%): 103 records/subjects; Validation/test (19.6 %): 25 records/subjects.	Age: 48.9 ± 10.5 yrs (range 37–82). Sex: 55.5% female (71 F/57 M); severity strata used in the model: mBI ≤ 30 (“severe”) and mBI ≤ 45 (“moderate”); SPMSQ 0–2, 3–4, ≥5.	Private/Institutional	[46]
Convolutional Neural Network (CNN)	99.70%	99.75%	99.70%	99.70%	10 subjects (80/20)	Age: 29.3 ± 5.85 yrs (5M/5F); condition: all participants were healthy	UI-PRMD Dataset—Public, University of Idaho repository	[47]

Table 3. Comparative performance analysis of multiple deep learning methods used in rehabilitation.

Methods	Accuracy	Recall	Precision	F1-Score	Training/Validation	Dataset Composition	Provenance/Accessibility	Reference
Proposed FFO-BI-LSTM	99.06	99.65	99	99.20	Not reported	Only illustrative values are shown	Private/Institutional (Real-time VR rehabilitation sessions collected by the authors’ lab)	[44]
Deep Neural Network (DNN)	92.50%	-	-	-	58 patients with shoulder diseases (80/20)	Age: 60.5 ± 9.7 (37–82); gender: 46.6% M, 53.4% F; conditions: adhesive capsulitis, rotator cuff disease; severity: The Shoulder Pain and Disability Index (SPADI) 43.9 ± 22.7 (0–100).	Private/Institutional (IMU-sensor data recorded at Seoul Metropolitan Government Boramae Medical Center, South Korea)	[45]
Artificial Neural Network (ANN)	86.4%	71.2%	-	-	128 subjects (one IRP per person); training (80.4%): 103 records/subjects; Validation/test (19.6 %): 25 records/subjects.	Age: 48.9 ± 10.5 yrs (range 37–82). Sex: 55.5% female (71 F/57 M); severity strata used in the model: mBI ≤ 30 (“severe”) and mBI ≤ 45 (“moderate”); SPMSQ 0–2, 3–4, ≥5.	Private/Institutional	[46]
Convolutional Neural Network (CNN)	99.70%	99.75%	99.70%	99.70%	10 subjects (80/20)	Age: 29.3 ± 5.85 yrs (5M/5F); condition: all participants were healthy	UI-PRMD Dataset—Public, University of Idaho repository	[47]

Table 4. Comparative performance analysis of multiple machine learning methods used in rehabilitation.

Methods	Accuracy	Recall	Precision	F1-Score	Training/Validation	Dataset Composition	Provenance/Accessibility	Reference
Two-Layer Neural Network	90.60%	-	-	-	1280 tests (16 subjects, LOO-CV)	Ages 8–75, 56% male/44% female, post-abdominal surgery rehab	IEEE DataPort (public)	[48]
Logistic Regression (LR)	100%	-	-	-	22 patients (70/30)	Ages 50–75, knee osteoarthritis (TKA), KOOS-ADL functional score	Private/Institutional (GDPR, request from authors)	[49]
Linear Discriminant Analysis (LDA)—Support Vector Machine (SVM)	86.98%	67.44%	67.84%	67.48%	645 samples (70/15/15 split), 43 participants	Healthy subjects, mixed gender, 5 gesture types	Public—GRABMyo dataset on PhysioNet	[50]
Genetic Algorithm-Based Clustering (GAClust)	100%	-	-	-	64 training/56 test	RTSA patients	Private/Institutional (Hygeia Group, Athens)	[51]
K-Nearest Neighbors (KNN)	99%	99%	99%	99%	96 training/24 test	RTSA rehabilitation metrics	Private/Institutional	[52]
Adaptive Boosting (AdaBoost)	84%	91%	-	65%	16 training/4 test	Children, psychological subgroup classification	Private/Institutional (physiotherapy clinic, on request)	[53]

Table 4. Comparative performance analysis of multiple machine learning methods used in rehabilitation.

Methods	Accuracy	Recall	Precision	F1-Score	Training/Validation	Dataset Composition	Provenance/Accessibility	Reference
Two-Layer Neural Network	90.60%	-	-	-	1280 tests (16 subjects, LOO-CV)	Ages 8–75, 56% male/44% female, post-abdominal surgery rehab	IEEE DataPort (public)	[48]
Logistic Regression (LR)	100%	-	-	-	22 patients (70/30)	Ages 50–75, knee osteoarthritis (TKA), KOOS-ADL functional score	Private/Institutional (GDPR, request from authors)	[49]
Linear Discriminant Analysis (LDA)—Support Vector Machine (SVM)	86.98%	67.44%	67.84%	67.48%	645 samples (70/15/15 split), 43 participants	Healthy subjects, mixed gender, 5 gesture types	Public—GRABMyo dataset on PhysioNet	[50]
Genetic Algorithm-Based Clustering (GAClust)	100%	-	-	-	64 training/56 test	RTSA patients	Private/Institutional (Hygeia Group, Athens)	[51]
K-Nearest Neighbors (KNN)	99%	99%	99%	99%	96 training/24 test	RTSA rehabilitation metrics	Private/Institutional	[52]
Adaptive Boosting (AdaBoost)	84%	91%	-	65%	16 training/4 test	Children, psychological subgroup classification	Private/Institutional (physiotherapy clinic, on request)	[53]

ML has also been used in core muscle rehabilitation and preoperative assessments via mobile accelerometer data. A computational methodology combining signal processing, feature extraction, and classification achieved a 90.6% accuracy in differentiating motion patterns, as shown by the data in Table 4 [48]. Another important aspect of technology development in healthcare is monitoring the recovery of patients after surgery, in particular after a total knee replacement (TKR). This area makes extensive use of inertial measurement units (IMUs) and trained classification models to track recovery dynamics based on a biomechanical data analysis during activities such as walking and stair climbing. While subjective patient assessments remain important, objective data generated by ML can provide additional valuable information about the recovery process and facilitate the development of more individualized rehabilitation approaches [49]. As reported in other scholarly work, significant advances are being made in the development of intuitive prosthetic control systems using EMG signals and ML methods. Studies conducted on datasets such as GRABMyo demonstrate that advanced signal processing techniques, including filtering and the discrete wavelet transform (DWT), combined with carefully selected feature sets and classification algorithms, such as SVM, can achieve a high gesture recognition accuracy. The quality and diversity of the datasets used play a primary role in ensuring the reliability and broad applicability of the developed models [50]. ML and computational intelligence algorithms have also been applied in the rehabilitation classification and prognosis for reverse total shoulder arthroplasty (RTSA) [50], where comparisons between KNN and GAClust with hybrid computational intelligence approaches revealed that genetic optimization enhances the classification performance while reducing dataset size requirements. In addition, a deep learning model utilizing an IMU sensor successfully classified 11 types of shoulder rehabilitation exercises, achieving a 92.5% test accuracy, as illustrated in Table 3, which demonstrates the feasibility of AI-driven exercise compliance monitoring for personalized therapy [45]. Feature reduction techniques, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), have also improved classification accuracy, achieving a 99% accuracy in leg rehabilitation using Random Forest (RF) and KNN, as demonstrated in Table 4 [52].

Research on personalized rehabilitation has leveraged multimodal physiological signals and psychological data to identify predictors of effective treatment. A mathematical model using an Adaptive Boosting (AdaBoost) algorithm demonstrated the potential for individualized therapeutic interventions through robust personalization techniques [52], achieving an 84% accuracy as reflected in Table 4. A subsequent analysis aimed at predicting the effectiveness of rehabilitation programs for orthopedic and neurological patients demonstrated the utility of tree-based ensemble machine learning models, showcasing the potential of AI for predicting functional improvement. Similar efforts have applied ML techniques to predict rehabilitation success using clinical and patient-reported outcome measures (PROMs) and clinical measurements (CROMs). Regression and classification algorithms estimating rehabilitation success based on admission and discharge differences have produced F-scores exceeding 65%. Applications of ANNs have further advanced rehabilitation outcome evaluations, particularly for patients with chronic neurological conditions. A retrospective study predicting improvements in the Barthel Index (BI) using demographic, clinical, and ICF (International Classification of Functioning, Disability, and Health) codes achieved an accuracy of 86.4%, identifying mobility codes as strong predictors, as reported in Table 3 [46,54,55].

Deep learning approaches have played a pivotal role in advancing home-based remote rehabilitation [56]. A deep learning system designed to assess rehabilitation exercises through a real-time motion capture analysis achieved high accuracies of 89% for the range of motion classification and 98% for the compensatory pattern recognition, indicating its potential to enhance access to quality rehabilitation care beyond clinical settings. Deep learning models such as the CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and CNN-LSTM demonstrated impressive performances on the KIMORE and UI-PRMD datasets, where the CNN achieved accuracy rates of 93.08% and 99.7%, respectively, as shown in Table 3 [47]. These results demonstrate the effectiveness of deep learning techniques in rehabilitation exercise classification and disease identification. The development of a responsible AI model designed to support home-based rehabilitation through a hybrid machine learning approach reinforces the emphasis on individualized rehabilitation, combining ensemble learning and hybrid stacking to ensure personalization, clear interpretability, and the alignment with ART design principles, all while keeping computational demands minimal. This model achieved a 100% accuracy in tasks like the Five Time Sit To Stand (FTSTS) test and Timed Up and Go (TUG) test and improved performance by 5% and 15%, respectively, compared to previous systems relying on intrusive monitoring [57].

3.3.2. Virtual Assistants and Chatbots Powered by LLMs for Patient Support

AI-powered chatbots have demonstrated positive results in enhancing self-management skills, reducing anxiety and improving patient satisfaction in clinical scenarios such as type 2 diabetes, oncology, and chronic pain management [58]. One emerging trend in digital health is the use of chatbots as the first point of contact within medical service systems, particularly among younger populations. A study focusing on student mental health [59] found that more than 50% of participants reported symptoms of anxiety and depression, with nearly half of them unaware of where to seek professional help. In this context, the anonymity and constant accessibility of chatbots are seen as important advantages. Chatbots have proven to be particularly impactful in emergency and crisis situations. During the COVID-19 pandemic, an AI/ML-based chatbot was developed for remote triage and risk assessment [59]. Field trials conducted in remote areas demonstrated its high efficiency in the symptom data collection, logical risk analysis, and decision support regarding physician referrals. The success of this system was attributed not only to its functional simplicity but also to its adaptability to local conditions, including its voice interface support and reliable operation in low-bandwidth environments. Further potential lies in combining chatbots with sensing and telemetry technologies. A study [60] presented a rehabilitation monitoring system based on Ultra-Wideband (UWB) radar and ML algorithms, achieving a 99.45% accuracy in detecting upper limb movement during exercise.

Parallel to these developments, the use of LLMs in healthcare has emerged as a highly promising and widely discussed domain. Models such as GPT-3.5, GPT-4, and domain-specific architectures have demonstrated the ability to process extensive volumes of medical text, participate in clinical analysis, generate structured conclusions, and even engage in human-like patient dialog. Recent research explored the application of ChatGPT-4 as a clinical tool in rehabilitation medicine, where it was tasked with generating rehabilitation prescriptions and ICF codes for a stroke case. The study highlighted both the strengths and limitations of the model, showcasing its potential for quickly generating rehabilitation plans while emphasizing the need for further refinement and human oversight in real-world clinical settings [61]. This work [62] builds on the growing interest in the application of natural language processing (NLP) algorithms for extracting and categorizing physical rehabilitation exercise data from clinical notes. One study developed and validated multiple NLP algorithms, demonstrating that rule-based and machine learning approaches could significantly enhance the rehabilitation data extraction from unstructured clinical records. This highlights the potential for NLP to automate and optimize rehabilitation therapy processes.

A comprehensive scoping review of over 550 publications [63] identified key areas of LLM application, including medical documentation automation, scientific text generation, educational assistance, biomedical research participation, and clinical information interpretation. Despite rapid progress, the need for empirical validation in real-world clinical settings remains critical. Empirical studies highlight both the potential and limitations of LLMs in medical contexts. Studies have shown that ChatGPT can effectively generate medical records, summarize the scientific literature, and engage in public health discussions [64]. However, it also demonstrated significant deficiencies in causal reasoning and occasionally generated unreliable content. Without explicit prompts, the model often fails to warn users about the potential inaccuracy of its outputs, posing risks in high-stakes domains, such as critical care or oncology. When tasked appropriately, LLMs can achieve an expert-level analytical performance. Their performance in rehabilitation systems has been evaluated using key metrics such as accuracy, precision, recall, and F1-scores, revealing differences between models, as shown in

Table 5. Performance comparison of different LLMs in rehabilitation platforms.

Methods	Accuracy	Recall	Precision	F1-Score	Training/Validation	Dataset Composition	Provenance/Accessibility	Reference
BloomZ-3B (Fine-tuned)	-	0.8355	0.7876	0.81	337,000 QA/578	Various health topics; CVD 26.05%, musculoskeletal 7.53%; no demographics or severity	Compiled from ViHealthQA, PubmedQA, etc.; Private/Institutional, on request	[66]
LLaMA2-7B (Fine-tuned)	-	0.8335	0.836	0.8343	337,000 QA/578	Same as BloomZ-3B	Same as BloomZ-3B	[66]
LLaMA2-13B (Fine-tuned)	-	0.8119	0.8109	0.8109	337,000 QA/578	Same as BloomZ-3B	Same as BloomZ-3B	[66]
GPT-4	87%	-	-	-	NR/3692, 123, 188 (EFIC), 102 interviews	EFIC interviews; no demographics or severity reported	OpenAI GPT-4 via Azure; data not shareable; Private/Institutional	[65]
GPT-3.5	93.4%	-	-	-	NR/64 × 2 (OMFS), 123 (EFIC), used in QA dataset	OMFS QA; EFIC interviews; no patient data or severity	OpenAI GPT-3.5 via Azure; Private/Institutional	[67]
Claude-Instant	95.2%	-	-	-	NR/64 × 2 (OMFS)	OMFS QA; no demographics or severity	Anthropic Claude-Instant; online; Private/Institutional	[67]
ChatGPT (zero-shot)	-	0.33	0.8	0.37	NR/50 notes (13,605 patients)	Stroke patients; age 75 ± 16, 51% female, some race/ethnicity data; no severity scale	UPMC stroke notes; IRB-approved; Private/Institutional	[62]
ChatGPT (few-shot)	-	0.27	0.82	0.35	NR/50 notes (13,605 patients)	Same as zero-shot	Same as zero-shot	[62]

. The models compared include GPT-4, BloomZ-3B (fine-tuned), LLaMA2-7B (fine-tuned), LLaMA2-13B (fine-tuned), GPT-3.5, Claude-Instant, and ChatGPT (in both zero-shot and few-shot). Performance data across various LLMs demonstrate the potential of LLMs during rehabilitation processes. In the study [65], GPT-4 was used to analyze open-ended patient texts in the context of Exception from Informed Consent (EFIC) research, achieving an 87% accuracy in thematic classification and emotional pattern recognition, as illustrated in Table 5.

This highlights the potential of LLMs to analyze patient feedback, assess subjective emotions, and evaluate trust in healthcare systems. Another important benefit of LLMs is their adaptability to linguistic and cultural contexts. In a study focused on low-resource language environments [66], fine-tuning a model on Vietnamese medical data significantly improved its communication quality with patients. The model not only responded more accurately to queries but also reduced the incidence of incorrect answers, thus increasing the accessibility of AI-driven tools for global health systems. However, ensuring the safety and reliability of LLM-generated medical content remains a significant challenge. A red teaming study [68] revealed that approximately 20% of ChatGPT’s responses to medical scenarios were classified as inappropriate or potentially harmful. Common errors included ignoring comorbidities, providing clinically inaccurate advice, and lacking sound justifications. These findings emphasize the importance of integrating LLMs into healthcare workflows under physician supervision and within robust risk management frameworks.

The practical utility of LLMs has also been evaluated in comparative clinical studies. In one study focused on decision support for maxillofacial surgery in patients with comorbidities [67], ChatGPT and Claude-Instant were benchmarked against expert opinions. The models’ responses aligned with experts in 89–95% of cases when queries were well-formulated, further highlighting the need for precise task formulation and continued human oversight in clinical decision-making.

As shown in

Table 6. A comparative analysis of language models on the hallucination rate and medical exam performance.

Model	Hallucination Rate	Medical Exam Score (USMLE): Step 1	Medical Exam Score (USMLE): Step 2	Medical Exam Score (USMLE): Step 3	References
GPT-4	28.6%	63.2%	64.3%	66.7%	[69,70]
GPT-3.5	39.6%	-	-	-	[70]
GPT-3.5 Turbo	-	42.1%	50%	50%	[69]
GPT-4V	-	84.2%	85.7%	88.9%	[69]

, comparative studies have directly evaluated several popular model families, including GPT-4, GPT-3.5, GPT-3.5-Turbo, GPT-4V, and LLaMA-2-13B.

On the Med-HALT stress test, which assesses the ability to answer medical questions without factual errors, GPT-4 exhibited the lowest hallucination rate (28.6%). Intermediate results were observed for GPT-3.5 (39.6%) [70].

The same models were tested on the official USMLE practice sets (Steps 1–3). The GPT-4V model achieved the highest scores, significantly exceeding the passing threshold across all three steps (84.2%/85.7%/88.9%). The base GPT-4 scored as follows: 63.2%, 64.3%, and 66.7% for Steps 1, 2, and 3, respectively. The results for GPT-3.5-Turbo were lower (42.1%/50%/50%). However, when augmented with the Retrieval-Augmented Generation (with PubMed access during inference), all three scores rose above 70%, underscoring the importance of an external knowledge integration rather than merely increasing the parameter count [69,70].

4. Discussion

Cardiac telerehabilitation (CTR) has been supported by multiple multicenter randomized controlled trials (RCTs) and longitudinal studies. A national registry-based cluster RCT compared remote versus center-based cardiac rehabilitation using long-term follow-up and economic data, demonstrating a rigorous multicenter design [71]. A meta-analysis of 14 RCTs reported that CTR significantly improved 6 min walk test (6MWT) performances, daily step counts, and exercise habit formation. It also modestly reduced depression and enhanced quality of life, with gains in both mental and physical health scores. Several of the included trials were multicenter and CONSORT-compliant [72]. In post-stroke rehabilitation, a pragmatic multicenter implementation study across seven sites found that robotic exoskeleton therapy led to substantial improvements in walking distances for patients with severe mobility limitations compared to conventional therapy [73]. Another multicenter, assessor-blinded RCT across three inpatient hospitals showed clinically meaningful improvements in gait metrics, such as the 6MWT [74]. Further supporting evidence from a large single-center study reported moderate to large effect sizes favoring powered exoskeleton therapy for cortico-spinal excitability and for gait quality and muscle activation [75].

Despite their immense potential, NLP-based models face several significant challenges when it comes to real-world implementation. The main limitations of LLMs, including ChatGPT, involve their unclear effectiveness in complex real-world scenarios, especially in medicine where high levels of reasoning are required. They are not designed specifically for medical issues and do not have sufficient medical expertise and context to fully understand the complex relationships between conditions and treatments. They often struggle to establish cause-and-effect relationships. There is a “hallucination” problem where ChatGPT may produce plausible but incorrect or nonsensical answers and also reproduce the biases contained in the training data. They cannot perform statistical analysis and rarely warn of limitations. In public health, their replies may depend on user input and can reflect stereotypes [64,68,76]. From the systematic review of 118 peer-reviewed papers on using LLMs to automate and support educational tasks, it was revealed that there is low technological readiness and a lack of replicability [77]. The existing research on LLMs in education lacks structured frameworks and focuses on isolated tasks, overlooking scalability and ethics. There is limited research on the adaptability across diverse contexts like K-12, distance, or blended learning. Real-world applications and efforts to address educational inequities remain largely unexplored [78].

Med-PaLM is a domain-specialized LLM developed using instruction prompt tuning, a parameter- and data-efficient method for aligning general-purpose LLMs to the medical domain. This approach uses a small, carefully curated set of medical questions–answer examples with clinician-reviewed, step-by-step reasoning to guide the model’s outputs. The goal is to minimize hallucinated or harmful content while ensuring that generated responses conform to clinical standards and established medical guidelines. Evaluations show that Med-PaLM significantly improves the factual alignment with scientific consensus, reduces the rate of harmful responses, and achieves a high-level performance on expert medical benchmarks such as MedQA. These findings suggest that instruction prompt tuning is a promising methodology for improving the safety, reliability, and clinical utility of LLMs in healthcare applications [79]. In a related effort to enhance performance in the medical domain, PMC-LLaMA introduces a data-centric fine-tuning framework that adapts general-purpose language models for clinical applications. It is trained on a large corpus comprising 4.8 million biomedical academic papers and 30,000 medical textbooks, enabling it to develop a deeper understanding of domain-specific terminology and clinical reasoning. The fine-tuning process includes instruction tuning with 202 million tokens covering medical question answering, rationale generation, and multi-turn dialogs. It improves factual accuracy and aligns with medical standards through curated, domain-specific instruction data. Moreover, PMC-LLaMA emphasizes modeling complex conversational patterns and includes systematic ablation studies to assess the impact of training components. Despite its relatively compact size (13 billion parameters), the model outperforms larger systems such as ChatGPT on public benchmarks [80].

Ethical and privacy concerns surrounding the use of NLP models are increasingly critical. Issues regarding data privacy, informed consent, algorithmic bias, and the potential misuse of data raise serious questions about responsible implementation. LLMs pose security risks, enabling side-channel attacks, phishing, and malware creation. They can leak private data and spread misinformation. Vulnerabilities like data poisoning make them more exploitable. Tools like FraudGPT and WormGPT show their potential misuse in cybercrime [81,82]. While LLMs have been criticized for their bias, hallucination, and misinformation, these concerns are often overstated or misunderstood. Bias exists in all human systems, and LLMs can actually help identify and reduce it when trained responsibly. Misinformation risks can be mitigated through fine-tuning, transparency, and ethical use [82]. To support LLM development without compromising privacy, data regulations should be more flexible using a tiered data risk model: public, internal, confidential, and restricted. Transparency, patient consent, and education on data rights are key. Addressing IP and data provenance requires licensed datasets, watermarking, and blockchains. Bias, hallucinations, and model reproducibility must be managed through evaluations, monitoring, and regulatory sandboxes [83].

In clinical environments where patient profiles and data acquisition methods continuously evolve, drift detection must be both proactive and robust to maintain AI model accuracy and fairness [84]. Modern systems use automated drift detection with ongoing performance checks, comparing metrics like accuracy and recall to set thresholds to flag drift [85]. Healthcare AI faces added challenges such as high-dimensional data, delayed ground truth, and clinical interventions. An effective re-validation requires coordination among clinicians, data scientists, and regulators to set performance standards. Tools like Shewhart and CUSUM charts help track changes and detect performance shifts [84]. Advanced drift detection techniques such as ensemble-based methods are effective in handling sudden, incremental, and recurring drift. These models adjust instance weights and parameters to maintain performance, as seen in triage systems where patient symptoms evolve over time [86]. Distinguishing between real and virtual concept drift is essential. Real drift changes the model’s decision boundary, while virtual drift affects input distributions without altering the decision logic [87]. Meta-learning and reinforcement learning enhance re-validation by enabling adaptive update strategies. Meta-learning captures historical drift to optimize retraining schedules, while reinforcement learning fine-tunes drift thresholds and the retraining frequency [85]. Dimensionality reduction techniques like PCA help isolate drift-sensitive components, improving detection efficiency [84,86].

Ethical considerations, long-term deployment issues, and real-world implementation challenges are central to the responsible use of IoT and sensor technologies. One of the foremost ethical concerns is privacy, defined as the individual’s right to control how their personal data is collected and used, particularly in systems involving wearables and healthcare digital twins [88]. To address this, developers are encouraged to adopt “privacy in design” principles, embedding privacy-enhancing technologies and transparent control mechanisms during the early stages of system development. Data ownership and stewardship also present significant ethical challenges, as the IoT data often flows across devices, cloud services, and international borders, raising complex legal and regulatory issues. Clear definitions of data ownership, robust access controls, and user awareness of who can access and use their data are essential [89]. In contexts such as smart healthcare, systems must also allow users to override automated decisions when they conflict with personal values or clinical best practices [88]. Long-term deployment introduces technical challenges, including the need for interoperable communication protocols that can dynamically adapt to changing environmental conditions [90]. Many IoT devices are resource-constrained, making the implementation of strong cryptographic protections without compromising performance particularly difficult. Security measures, including intrusion detection systems, must continuously evolve to keep pace with new attack vectors, requiring ongoing updates and vigilance [91]. Real-world implementation also demands clear governance models, especially in multi-user environments. These deployments must be guided by transparent policies regarding data access, user consent, and ownership, with ethical oversight mechanisms ensuring adherence to established standards [92,93].

The deployment of large-scale IoT networks involves billions of sensors, actuators, and communication modules, each contributing to both operational and embodied energy footprints. Energy challenges are intensified by device distributions in remote settings and the dependence on batteries or energy harvesting technologies. Notably, a significant portion of energy is consumed not during active transmission, but in maintaining the network connectivity, even in idle states [94]. The production and disposal of IoT devices add to embedded energy costs and electronic waste, which further increases their environmental footprint [95]. Complex AI systems—particularly LLMs—require high computational power during both training and inference phases [96], demanding specialized hardware such as GPUs and TPUs. This elevates energy consumption substantially. Data centers supporting these operations face environmental pressure due to their high Power Usage Effectiveness (PUE), with even minor inefficiencies resulting in large-scale carbon impacts [96,97]. Although virtualization and dynamic resource management have improved efficiency, the reliance on non-renewable energy remains a challenge [96]. Sustainable solutions under exploration include ultra-low-power chips, renewable energy harvesting, neuromorphic processors, and wafer-scale engines—though many remain experimental [94,98]. Edge computing and distributed cloud infrastructures offer an additional promise by reducing the transmission overhead and bringing computation closer to users, thus improving energy efficiency without compromising performance [97].

The digital divide today extends beyond affordability and includes factors such as digital literacy, cultural norms, geographic isolation, and disability-related access issues [98,99]. Even when the internet and devices are available, the lack of digital skills and contextual resources limits their meaningful use. Digital literacy—comprising technical, operational, and critical evaluation skills—is a major non-economic factor, with low education levels strongly linked to reduced usage [99]. Clinical trials frequently establish inclusion criteria that mandate a baseline level of computer literacy. The Universidad de Granada study [100] required that breast cancer survivors possess basic computer skills or, alternatively, have access to assistance from a literate family member. Similarly, the Euleria Home^® study [101] mandated that stroke survivors not only have access to an ADSL or faster internet connection but also demonstrate either a sufficient technological aptitude or rely on familial support to operate the telerehabilitation system. While such criteria are intended to ensure effective participation in digitally delivered interventions, they inadvertently exclude individuals with limited or no prior exposure to digital technologies—despite these individuals potentially standing to benefit significantly from remote rehabilitation services. Geographic remoteness further restricts access through poor infrastructure and limited support, exacerbating skill gaps and digital exclusion [39,40]. Research in African rural areas underscores that limitations in electricity supplies and the unstable internet connectivity due to infrastructural shortcomings significantly limit the effective implementation of telerehabilitation programs [30]. Moreover, even in regions that ostensibly have access to broadband services, the quality and reliability of connections may be insufficient to support resource-intensive applications like real-time video conferencing, leading to session disruptions and reducing the overall efficacy of telerehabilitation [41]. Individuals with disabilities face unique challenges that further contribute to the digital divide. Patients with physical impairments may require assistive technologies or adapted interfaces not typically provided by standard digital devices, while those with cognitive disabilities may have difficulty comprehending or following digital instructions without direct human intervention [102,103]. In a study focused on musculoskeletal disorders, physiotherapists reported that elderly patients faced considerable difficulties in navigating video conferencing platforms, often requiring additional support from caregivers to engage effectively in telerehabilitation sessions [104]. In addition, studies have identified that non-English speakers and individuals from ethnic minority groups frequently encounter barriers in telehealth settings, primarily due to language discrepancies and the lack of culturally responsive user interface designs [105]. Poorly designed technology interfaces also deter use among low-literacy populations [106].

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. Technologies based on biocompatible, self-powered sensors are only beginning to emerge from the laboratory. Most AI models are still trained on small, demographically homogeneous datasets, and clinical trials are often single-center and rely on surrogate endpoints. Without large-scale, multicenter randomized clinical studies reporting hard clinical outcomes—such as patient independence and readmission rates—it is impossible to achieve the regulatory approval and widespread adoption of these technologies. Overcoming these limitations will require the development of interoperable standards, privacy-first system architectures, expanded and diverse training datasets, mandatory external model validation, and the creation of durable, self-powered sensors. Ethically aligned LLM interfaces must be implemented under human oversight, and economic models must ensure equitable access to these technologies.

The miniaturization of devices and their integration with smartphones, laptops, and other interfaces provide both patients and clinicians with real-time access to physical activity and health monitoring. These technologies enable patients to report their condition, receive timely recommendations, and allow doctors to intervene immediately in case of adverse changes. Integrating such technologies into rehabilitation systems significantly improves patient satisfaction with care and facilitates the implementation of more convenient and individualized medical services. For the effective deployment of such systems, it is essential to continue improving the technology while adhering to ethical and legal standards. Only by addressing these challenges can the prototypes described in this review evolve into mature, safe, and scalable rehabilitation platforms capable of transforming recovery trajectories around the world.