## 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 (6 MWT) performances, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Hallucination Rate**} & **Medical Exam** & **Medical Exam** & **Medical Exam** & **References** \\  & & **Score (USMLE):** & **Score (USMLE):** & **Score (USMLE):** & **References** \\  & & **Step 1** & **Step 2** & **Step 3** & \\ \hline 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] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparative analysis of language models on the hallucination rate and medical exam performance. 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 6 MWT [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.