## 4 Discussion  
### Direction of Implementation of AI-Based AM inRehabilitation and Physiotherapy Design  
The introduction of AI into AM in rehabilitation and physical therapy has led to significant technological advances. AI-based generative design tools have enabled the creation of highly complex, customized rehabilitation devices optimized for individual needs. ML algorithms improve material utilization, increasing the efficiency and sustainability of 3D-printing processes. AI-powered real-time monitoring systems increase quality control, reducing the likelihood of defects and ensuring precise production results. AI integration has accelerated the development and iteration of personalized solutions, shortening the design-to-production cycle. Advanced AI analytics predicts patient-specific treatment needs, enabling dynamic updates to device designs as conditions evolve. AI-powered digital twins simulate the performance of rehabilitation devices, enabling proactive improvements without physical testing. Robotics combined with AI in AM systems has increased automation, increasing scalability and reducing manufacturing costs [61,62]. These technologies have expanded the range of materials and techniques used in AM, enabling the production of biocompatible and durable devices. However, relying on cutting-edge AI also introduces challenges, such as the need for high computing power and specialized knowledge. The convergence of AI and AM is driving a new era of precision, efficiency, and accessibility in personalized rehabilitation technologies [63,64]. Integrating AI with AM for personalized rehabilitation and physical therapy has profound practical and clinical implications. AI-based customization enables the creation of devices that precisely meet individual anatomical and functional requirements, improving patient comfort and therapeutic outcomes. This innovation enables rapid prototyping, significantly reducing the time it takes to deliver personalized solutions, which is critical for patients who need urgent care [65]. Clinicians can use AI tools to simulate and test rehabilitation devices virtually, ensuring their effectiveness before physical production. Personalized assistive devices such as orthoses, prostheses, and exoskeletons improve patient mobility and independence, improving overall quality of life.AI-based AM facilitates the dynamic customization of devices, allowing them to evolve with the patient's recovery process, ensuring lasting effectiveness [66]. By optimizing material utilization and reducing waste, these technologies also make personalized solutions more cost-effective and environmentally friendly. AI-based monitoring and real-time quality control during production ensure the high reliability and consistency of medical devices. The introduction of these technologies is fostering interdisciplinary collaboration, encouraging healthcare professionals to adopt more innovative and data-driven approaches [67]. However, the increased reliance on advanced technology requires specialized training for clinicians, potentially disrupting traditional practices. Overall, the synergy of AI and AM is raising the standard of care in rehabilitation and physical therapy, providing tailored, effective, and accessible solutions for a variety of patient needs [68]. Integrating AI with AM for personalized rehabilitation and physical therapy raises significant social and ethical concerns. On the positive side, it promotes inclusivity by offering customized solutions, improving access for patients with unique conditions. However, the high costs of AI-based AM technologies may exacerbate healthcare inequities by limiting access for underserved populations. The extensive collection and use of patient data for AI modeling raises privacy concerns, requiring robust safeguards to protect sensitive information. Ethical dilemmas regarding liability arise, particularly in cases where AI-designed devices do not function as intended.