## 3 Results
### New Materials and Technologies of Rehabilitation Devices  
The integration of AI with AM has enabled the development of new materials and technologies for personalized rehabilitation devices. AI-driven design optimization enables the use of advanced, lightweight materials such as carbon fiber composites and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{table}
\begin{tabular}{c c} \hline \hline
**AI** & **Area of Application** \\ \hline Convolutional & \begin{tabular}{c} Used to analyze 3D scans and medical imaging data, enabling the precise \\ modeling of anatomical structures to create patient-specific prosthetics \\ and orthoses. \\ \end{tabular} \\ \hline Recurrent & \begin{tabular}{c} Analyze time-series data from patient movement patterns to inform the \\ design of adaptive rehabilitation devices that respond to dynamic needs. \\ \end{tabular} \\ \hline Generative & \begin{tabular}{c} Generate realistic 3D models of rehabilitation devices by learning from \\ existing datasets, creating innovative designs optimized for function, \\ aesthetics, and patient comfort. \\ \end{tabular} & \begin{tabular}{c} Generate realistic 3D models of rehabilitation devices by learning from \\ existing datasets, creating innovative designs optimized for function, \\ aesthetics, and patient comfort. \\ \end{tabular} \\ \hline Autoencoders & \begin{tabular}{c} Compress and reconstruct 3D data, facilitating the efficient storage and \\ refinement of complex rehabilitation tool designs. \\ \end{tabular} \\ \hline Bayesian networks & \begin{tabular}{c} Analyze uncertainties in patient-specific data, such as changes in body \\ dimensions or material tolerances, to create robust solutions suitable for \\ 3D printing. \\ \end{tabular} & \begin{tabular}{c} Analyze uncertainties in patient-specific data, such as changes in body \\ dimensions or material tolerances, to create robust solutions suitable for \\ 3D printing. \\ \end{tabular} \\ \hline Evolutionary & \
\begin{tabular}{c} Explore multiple design possibilities for 3D-printed rehabilitation aids, \\ evolving toward solutions that best meet therapeutic and ergonomic goals. \\ \end{tabular} \\ \hline Knowledge graphs and &
\begin{tabular}{c} By combining data on patient needs, material properties, and device \\ functionality, these AI techniques support informed decision-making when \\ designing and 3D-printing physical therapy solutions. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(Cont.\)thermoplastic elastomers that provide both strength and flexibility. These materials can be tailored to specific rehabilitation needs, providing a balance of durability and comfort for devices such as custom orthoses and prosthetics [41]. In addition, AI is helping to identify and develop biocompatible materials that can safely interact with the human body, making them ideal for long-term use in devices such as implants or internal supports. AI is enabling the creation of smart materials that can respond to environmental stimuli such as pressure or temperature, adapting to the needs of the patient in real time. For example, smart textiles and flexible electronics embedded in rehabilitation devices can monitor physiological parameters such as muscle activity and joint movement, providing valuable feedback to clinicians and patients [42]. This opportunity also includes the development of self-healing materials that can automatically repair minor damage to devices, extending their lifespan and ensuring continued functionality. The introduction of advanced 3D-printing techniques such as multi-material printing enables the production of devices with different properties within a single structure, such as soft cushioning and rigid support in a single prosthesis or orthosis [43]. AI is also helping to optimize the use of recycled and sustainable materials, contributing to more environmentally friendly manufacturing processes.