## 3 Results
### AI and ML for Rehabilitation Systems
#### 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 demonstratedits 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.