## 4 Discussion
### Future Directions  
The findings of the studies included in this systematic review are limited by the small sample sizes in many studies--often with few or no real participants--which restricts the generalizability of the results. To advance the role of generative AI in exercise prescription, future research must explore larger, more diverse populations, incorporating real-world data to validate AI-generated exercise recommendations. Further, it should prioritize several key areas. First, it should explore the integration of real-time adaptability mechanisms, which is essential for improving the specificity and progression of AI-generated recommendations. This could involve the use of cutting-edge techniques such as reinforcement learning or hybrid models that combine generative AI with sensor-driven data from wearable devices, enabling continuous monitoring and adaptation based on an individual's performance and physiological responses [54; 55; 56; 63; 64]. By leveraging real-time biometrics, such as heart rate variability, muscle fatigue markers, and motion analysis, AI models can dynamically adjust exercise intensity, duration, and recovery periods to optimize training effectiveness while minimizing injury risks. Additionally, these adaptive systems can personalize recommendations based on an individual's unique physiological traits, ensuring that exercise programs are not only inclusive but also tailored to the specific needs of diverse populations, including female athletes and individuals with disabilities. This can be achieved by incorporating advancements in exercise science, including novel training methodologies and population-specific adaptations. This necessitates collaboration between AI developers and domain experts to ensure that emerging evidence is seamlessly integrated into the AI's knowledge base [66]. Additionally, efforts to minimize biases and enhance inclusivity must be central to the development process. This includes addressing age, gender, and disability-related disparities in AI recommendations and ensuring that these systems are equitable and accessible to all [67]. Only a few studies explored these aspects, including sex-specific differences in AI-generated exercise prescriptions. For instance, the study by [PERSON] [42] revealed that AI-generated calisthenics training programs had a differential impact on male and female participants, with significantly better improvements observed in males for flexibility and muscular endurance compared to females. This suggests that current generative AI systems may not adequately incorporate sex-specific physiological and biomechanical differences when designing exercise regimens. Several factors could contribute to this limitation. AI models trained on general fitness datasets may underrepresent sex-based variations in metabolic responses, muscle composition, hormonal influences on recovery, and biomechanical differences affecting exercise execution. The absence of such considerations could lead to suboptimal or even counterproductive recommendations for female athletes and recreational exercisers. Moreover, the ethical and regulatory landscape surrounding AI in exercise prescription and healthcare requires significant attention [67]. Establishing robust frameworks for data privacy, accountability, and liability is critical to building trust among users and stakeholders. Given that AI-driven exercise recommendations rely on vast amounts of personal health data, ensuring compliance with global data protection laws such as GDPR and HIPAA is paramount. This includes implementing stringent encryption protocols, secure data-sharing agreements, and transparent consent mechanisms to protect user privacy. Additionally, clear guidelines must be developed to define the scope of AI's responsibility versus human oversight in exercise prescription, ensuring that healthcare providers and fitness professionals retain decision-making authority while benefiting from AI-driven insights.