## 3 Results
### Systematic Literature Review Findings
Studies that assessed the quality of AI-generated programs against expert-designed interventions consistently reported that AI output required expert validation and modifications to optimize effectiveness. While ChatGPT-created plans adhered to foundational training principles, they were frequently generic and did not incorporate real-time feedback or individualized adjustments. AI-generated running and strength training plans improved when additional user input was provided, but they still fell short of human expert-designed programs [44; 46; 48]. Furthermore, while AI-generated programs generally adhered to recognized exercise guidelines, some studies identified limitations in specificity, including an overemphasis on safety and moderate intensity at the expense of progression and individualization [43; 50]. In studies that examined physiological outcomes, AI-generated programs demonstrated positive effects in certain domains. Improvements were observed in BMI reduction, heart rate response, and muscular endurance [42; 47]. However, in direct comparisons with human-designed interventions, AI-generated exercise prescriptions were less effective in improving cardiovascular endurance and lower-body muscular strength, particularly in males [42]. Additionally, some AI-generated recommendations displayed biases, including an excessive emphasis on medical clearance requirements, which disproportionately affected older adults and individuals with disabilities [36]. When comparing different AI models, GPT-4 demonstrated superior performance relative to GPT-3.5 in generating tailored and structured resistance training programs,incorporating more advanced training concepts such as block periodization and active recovery [48]. The comparison between Google Gemini and GPT-4 showed that GPT-4 produced higher-quality training plans regardless of input specificity [46]. Nonetheless, reproducibility varied, and expert oversight was deemed essential to ensure the quality and applicability of AI-generated exercise recommendations. Further details are presented in Table 2.  
In terms of critical appraisal (Table 3), the studies analyzed exhibit varying degrees of methodological rigor and comprehensiveness, particularly concerning model specification, evaluation approach, timing of testing, transparency of data sources, and overall quality. A few studies, such as those by [PERSON] et al. [44], [PERSON] et al. [46], and [PERSON] et al. [50], demonstrate high methodological robustness, incorporating systematic topic selection, strong interrater reliability, and high prompt specificity. These studies also exhibit transparency in their data sources, ensuring replicability and credibility. In contrast, studies such as [PERSON] and [PERSON] [45], [PERSON] et al. [47], [PERSON] et al. [49], and [PERSON] et al. [51] display notable methodological weaknesses, particularly in terms of transparency, topic selection, and reliability. The lack of systematic topic selection and insufficient prompt specificity in these studies contribute to their lower overall quality, limiting their applicability and generalizability. Other studies, such as those by [PERSON] et al. [43] and [PERSON] et al. [48], occupy an intermediate position, meeting several methodological criteria but lacking in aspects such as topic selection and interrater reliability. A key differentiating factor among the studies is the range of tested topics and whether topic selection followed a systematic or randomized approach. Studies with a systematic approach tend to yield more reliable and reproducible findings, as demonstrated by [PERSON] et al. [44] and [PERSON] et al. [46]. Conversely, those lacking a structured topic selection process, such as [PERSON] et al. [47] and [PERSON] et al. [51], exhibit greater methodological limitations. Prompt specificity emerges as another critical determinant of study quality. While high-quality studies employ precise and well-structured prompts, lower-quality studies, such as those by [PERSON] et al. [49] and [PERSON] et al.