## 4 Discussion
### Limitations  
Integrating AI with AM for personalized rehabilitation and physical therapy solutions faces several limitations. One significant challenge is the high cost of advanced AI technology and equipment, which can limit accessibility for smaller healthcare providers [71; 72]. The need for large datasets to train AI models is another hurdle, as acquiring patient-specific data can be time-consuming and privacy-sensitive. Ensuring data security and compliance with regulations (e.g., EU regulations) is critical, but adds complexity to the implementation process [73; 74]. AI models sometimes struggle to accurately predict outcomes for different patient populations, leading to potential errors in personalized designs. Relying on specialized expertise to operate AI-based systems can also slow adoption, as healthcare workers may require extensive training. Real-time monitoring and defect detection, while promising, are not foolproof and can still result in defective products. Material limitations in 3D printing, such as biocompatibility and durability, can limit the applicability of AI-designed solutions [75]. In addition, integrating AI into existing rehabilitation processes could disrupt traditional practices, requiring significant process changes. The rapid evolution of AI technology raises concerns about obsolescence and the need for constant updates to remain effective [76]. Ethical issues surrounding the use of AI in healthcare, such as the accountability of decision-making, remain unresolved, complicating widespread acceptance in rehabilitation and physical therapy contexts.  
Despite its promise, integrating AI with AM for personalized rehabilitation and physical therapy reveals several gaps in current research. One major gap is the lack of large, diverse datasets for training AI models, which limits their ability to design solutions that span different demographics and conditions. The development of biocompatible, durable, and versatile materials for 3D-printing personalized medical devices remains underexplored. Research into real-time monitoring and error correction during AM processes is still in its infancy, leading to inconsistent product quality. Another gap concerns the ethical and regulatory framework for the use of AI in healthcare, particularly with regard to data privacy, liability, and equitable access. Research on the long-term effectiveness and safety of AI-based rehabilitation solutions is limited, and there are insufficient clinical trials to allow for the validation of results [77]. The integration of AI-AM systems into existing healthcare workflows has not been adequately addressed, leaving practical implementation strategies underdeveloped. Interdisciplinary collaboration between engineers, clinicians, and AI experts is often fragmented, slowing innovation and practical application [78]. Cost-cutting strategies for AI-AM systems remain underexplored, making these technologies inaccessible to many healthcare providers and patients. Additionally, the potential for algorithmic bias in AI systems is underexplored, raising concerns about the fairness of patient outcomes. Finally, research is needed to train and upgrade healthcare specialists to effectively use these advanced technologies in clinical settings [79]. Addressing these gaps is key to unlocking the full potential of AI-AM in personalized rehabilitation and physical therapy.