## 2 Related Work
### AI-Povered Digital Tools and Interfaces  
In the treatment of NDDs, digital platforms enhanced by AI are increasingly used to maximize real-time monitoring and encourage patient involvement. These systems frequently integrate cloud-based analytics with wearable and ambient sensing devices to support data-driven clinical decisions, personalized care, and early detection.  
Platforms like RO-SmartAgeing and PERFORM, which showcase various but complementary technological approaches, serve as examples of this potential. While PERFORM uses embedded microsensors and remote analysis frameworks to enable real-time motor symptom tracking in PD and amyotrophic lateral sclerosis (ALS), RO-SmartAgeing integrates sensor-based cognitive monitoring for aging populations [3; 14]. Machine learning-enabled wearable systems that detect motor impairments in real-world settings by gathering multimodal data like posture, tremor frequency, and gait characteristics are examples of further developments [15]. These tools aid in prompt and well-informed therapeutic interventions by identifying functional changes that might not be apparent during routine clinical evaluations, particularly in MS and PD [16]. For example, in MS, AI systems that combine EEG data, physiological metrics, and gait variability have been used to predict relapses and customize treatment plans. Long short-term memory (LSTM)-based models have been used in PD to improve real-time analysis of abnormalities in gait and tremor [17].  
Current advancements also include conversational and mobile AI applications for remote cognitive evaluation. To increase accessibility and user engagement, one such system uses chatbot interfaces integrated into mobile apps to help older adults in rural areas with cognitive screening and personalized training [38]. The CarePredict Tempo [39], a wrist-worm AI device, detects behavioral deviations that may indicate cognitive decline or increased fall risk, while radar-based noncontact solutions like Vayyar Home [40] continuously detect fall events by analyzing spatial movement patterns.  
To provide users and caregivers with adaptive, patient-specific feedback and encourage more individualized care strategies, multimodal platforms like Dem@Care [41] integrate physiological and environmental data. In the meantime, Smart Aging [42] uses serious games to incorporate cognitive evaluation into 3D virtual environments, comparing gameplay metrics like accuracy, time, and distance with standardized cognitive tests like the MMSE and MoCA. In nonclinical contexts, this gamified method facilitates ongoing, easily accessible, and entertaining cognitive function monitoring.  
While these systems represent a growing interest in intelligent, user-centered neurodegenerative care, their degree of integration, technical scope, and clinical applicability vary greatly For instance, PERFORM focuses on motor symptom detection without integrating cognitive elements; Dem@Care offers extensive sensor data fusion but lacks predictive analytics; and Smart Aging, although useful for gamified cognitive screening, does not allow for continuous physiological monitoring. These limitations underscore the need for more comprehensive and ethically sound platforms, such as NeuroPredict, which support multimodal, real-time, and personalized monitoring in line with clinical and regulatory requirements.  
The digital solutions illustrate the growing role of AI-powered interfaces in transforming neurodegenerative disease care. From diagnosis to daily support, these technologies contribute to more proactive, efficient, and personalized healthcare delivery. By combining real-time physiological sensing, cognitive evaluation, and AI-powered predictive modeling into a single, modular system, the NeuroPredict platform aims to get around these restrictions. Its design also complies with ethical and legal standards, making it a more complete and scalable option for managing MS, PD, and AD in realistic environments.