## 1 Introduction  
Additive manufacturing (AM), commonly known as 3D printing, is a revolutionary technology that enables the creation of three-dimensional objects layer by layer using digital models [1]. Artificial intelligence (AI) is increasingly being integrated into this field, bringing transformational improvements in efficiency, precision, and innovation. AI is streamlining every step of the AM process, from design optimization to real-time quality control. For example, AI-based generative design tools can create complex, lightweight structures that are both functional and material-efficient. These designs are often impossible to achieve using traditional engineering methods [2; 3]. In materials science, AI is accelerating the discovery of new materials suitable for specific AM applications by analyzing massive datasets. Machine learning (ML) algorithms predict how materials will behave under different conditions, enabling the selection of optimal compositions for durability and performance [4]. During the manufacturing process, AI helps monitor and control printing parameters, ensuring high-quality results and reducing waste [5]. Real-time defect detection using AI-powered sensors and imaging technologies minimizes the risk of defective products. Additionally, AI supports the predictive maintenance of 3D printers by analyzing performance data to predict failures and proactively plan repairs [6; 7]. Integrating AI with AM also facilitates mass customization, enabling the efficient production of personalized items in industries such as healthcare and consumer goods. Robotics and AI work together to enable autonomous manufacturing systems, increasing scalability and reducing human intervention [8]. In supply chain management, AI enhances inventory control and production planning, ensuring a smooth flow of raw materials and finished products [9]. AI-powered simulations help manufacturers test and refine designs before printing, saving time and resources [10]. AI-powered digital twins replicate physical AM processes to predict outcomes and improve decision-making [11]. The synergy of AI and AM also contributes to sustainability by optimizing energy consumption and material recycling. This combination opens up new opportunities in sectors such as aerospace, automotive and medical, where precision and innovation are paramount. The future of AM is being shaped by AI, promising smarter, faster, and more sustainable production methods. As AI advances, its integration with AM is expected to lead to unprecedented advances in technology and industry [1]. The rise of AM in personalized physical therapy and rehabilitation solutions represents a significant advance in healthcare [12]. Initially, traditional rehabilitation tools were often generic and lacked personalization, limiting their effectiveness for individual patients. The introduction of AM changed this paradigm, enabling the production of personalized medical devices tailored to a patient's unique anatomy [13]. The early adoption of AM in rehabilitation began with the development of custom orthopedic devices, such as braces and splints, that offered improved fit and comfort compared to mass-produced alternatives. As technology advanced, 3D scanning and imaging were integrated with AM, enabling the precise measurement of the patient's body. This facilitated the creation of highly personalized prosthetics and orthoses designed for specific functional needs [14]. For example, lightweight and ergonomic prosthetic limbs could be 3D-printed to increase mobility and reduce burden. Another breakthrough was the development of custom exoskeletons and support structures to assist patients with impaired mobility or neuromuscular disorders [15; 16]. The flexibility of AM has also enabled rapid prototyping anditerative improvements to rehabilitation devices based on patient feedback. Custom gripping aids designed for people recovering from hand injuries or strokes have demonstrated the adaptability of 3D printing to a variety of treatment needs [15].