## 3 Results
### AI and ML for Rehabilitation Systems
#### 3.3.2 Virtual Assistants and Chatbots Powered by LLMs for Patient Support
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. 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.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **Training/Validation** & **Dataset Composition** & **Provenance/Accessibility** & **Reference** \\ \hline BloomZ-3B (Fine-tuned) & - & 0.8355 & 0.7876 & 0.81 & 337,000 QA/578 & & Various health topics; & Compiled from \\  & & & & & & CVD 26.05\%, & ViHealthQA, PubmedQA, & \\  & & & & & & musculoskeletal 27.53\%; no & & etc. ; Private/Institutional, & \\  & & & & & & & demographics or severity & on request & \\ LLMaM2-7B & - & 0.8335 & 0.836 & 0.8343 & 337,000 QA/578 & Same as BloomZ-3B & Same as BloomZ-3B & [66] \\  & & & & & & & \\ LLMaM2-13B & - & 0.8119 & 0.8109 & 0.8109 & 337,000 QA/578 & Same as BloomZ-3B & Same as BloomZ-3B & [66] \\  & & & & & & NR/3692, 123, 188 & EFC interviews; no & OpenAI GPT-4 via Azure, & \\  & & & & & (EFIC), 102 & demographics or severity & data not shareable; & [65] \\  & & & & & & & reported & Private/Institutional \\  & & & & & NR/64 \(\times\) 2 (OWTS), & OMRS QA; EHC & OpenAI GPT-3.5 via & \\ GPT-3.5 & 93.4\% & - & - & - & 123 (EFIC), used in & interviews; no patient data & Azure; & [67] \\  & & & & & QA dataset & or severity & Private/Institutional \\  & & & & & & & OMRS QA; no & Anthropic Claude-Instant; & \\ Claude-Instant & 95.2\% & - & - & - & NR/64 \(\times\) 2 (OWTS) & OMRS QA; no & & online; & [67] \\  & & & & & & demographics or severity & Private/Institutional \\  & & & & & & & Stroke patients; & UPMC stroke notes; \\  & & & & & & & & IRB-approved; & [62]  
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, 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.