## 4 Discussion
### Open Issues and Future Perspectives  
However, despite the aforementioned advancements of AI and HCI, several challenges and future research directions remain. To begin with, the emergence of large language models (LLMs) presents new opportunities for enhancing virtual companionship, adaptive learning, and personalized therapeutic interventions, yet their integration into clinical and assistive settings requires further validation [64]. Additionally, the applicability of AI-driven solutions in real-world scenarios is often hindered by high implementation costs, technical complexities, and the need for seamless integration into existing healthcare infrastructures [65].  
Moreover, as AI-driven HCI technologies are increasingly integrated into OT, several ethical concerns shall be addressed to ensure their responsible and equitable application [66]. One key issue constitutes data privacy, where the collection, storage, and processing of sensitive patient data raise significant concerns about how these data are protected and whether individuals' rights are adequately safeguarded [67]. It is of the utmost importance to ensure that AI systems adhere to strict data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe [68], and maintain patients' confidentiality. Another crucial ethical challenge is algorithmic bias. If not properly designed, AI algorithms may inadvertently perpetuate existing biases in healthcare, leading to inequitable treatment outcomes for certain groups, particularly those from marginalized or under-represented populations [69]. It is essential that AI systems are regularly audited and tested to ensure they are fair and unbiased in their decision-making [70]. Finally, technological accessibility remains a pressing issue. Although AI technologies hold great promise for enhancing OT interventions, they may not be equally accessible to all patients, particularly in low-resource settings or among individuals with limited technological literacy [71]. Efforts to develop user-friendly and accessible AI tools are necessary to ensure that these advancements benefit a broad range of patients, including those with physical or cognitive impairments [72].  
Building on the aforementioned concerns, several specific strategies can be proposed to mitigate associated risks. To protect data privacy, robust encryption standards, secure data storage practices, and strict access control measures should be implemented [73]. Meanwhile, the use of diverse, representative datasets and regular audits of AI models are critical to ensure fairness and transparency, thus countering algorithmic bias [74]. Then, the promotion of equitable access requires the design of affordable, intuitive technologies alongside initiatives that improve digital literacy among patients and providers [75]. Therefore, it becomes evident that interdisciplinary collaboration among AI researchers, healthcare professionals, and policymakers will be essential to address these challenges in a comprehensive way, ensuring that the integration of AI and HCI into OT is not only innovative but also ethical, inclusive, and sustainable [76].