## 3 Results  
### Systematic Literature Review Findings  
A total of 2891 records were initially identified from database searches. Before the screening, 1619 records were removed as duplicates. No records were excluded based on automation tools or other reasons. Following duplicate removal, 1272 records remained for screening. After screening, 1211 records were excluded, leaving 61 reports for retrieval. All 61 reports were successfully retrieved, with none being unavailable. Subsequently, 61 reports were assessed for eligibility. Ultimately, 10 studies [42; 43; 44; 45; 46; 47; 48; 49; 50; 51] were included in the systematic review. Further details are pictorially shown in Figure 1.  
The studies represented a diverse geographic distribution across North America, Europe, Asia, and Africa. Among them, one study involved a multi-national collaboration, with authors from 20 countries [43], while others were conducted mainly in Germany [44; 46], Turkey [45], China [49; 51], Thailand [47], the Philippines [42], and the USA [50]. Finally, a study was multi-country [48]. The studies employed a range of methodologies, including qualitative/quasi-qualitative assessments and expert panel reviews [43; 48; 51], quantitative assessments [45], mixed-methods approaches [44; 46; 49; 51], a quasi-experimental design [42], and a randomized controlled trial (RCT) [47], reflecting the evolving nature of research on AI-generated exercise prescription. Figure 1: Pictorial flowchart of the present systematic review and meta-analysis. The studies primarily examined the capabilities of different AI models in generating exercise programs [43; 47; 49; 50; 51] or training plans [29; 44; 45; 46; 48] and their performance relative to human experts. ChatGPT was the most frequently evaluated AI system, with various versions included across eight studies. GPT-4 was assessed in several studies [43; 46; 47; 48; 49; 51], while ChatGPT-3.5 was compared in others [42; 45; 48; 50]. One study [46] included Google Gemini, finding that GPT-4 outperformed it in training plan quality. One study [44] used ChatGPT-3. Finally, another study [49] compared ChatGPT to the Intelligent Health Promotion Systems (IHPS), demonstrating that ChatGPT excelled in accuracy and comprehensiveness, although IHPS had greater consistency in applicability. The study populations varied significantly, ranging from expert evaluations without real participants [43; 44; 45; 46] to a few empirical studies [42; 47; 49]. Two of these involved small samples, with five [49] and nine [47] participants, respectively. Larger participant groups were included in [42], which examined 87 individuals. Demographic considerations were generally limited, with the exception of a single study [50] that analyzed exercise recommendations across 26 diverse populations. These included healthy adults, older individuals, pregnant individuals, and those with chronic conditions such as cardiovascular disease, diabetes, cancer, and HIV. The AI-generated exercise prescriptions were assessed using various frameworks, including adherence to the FITT standardized training principles such as periodization, training volume, and intensity [43; 46; 48; 50; 51], or the ACSM guidelines [50; 51]. Additionally, evaluations considered factors like accuracy and adequacy [45; 49; 50]. Notably, the criteria used for assessment were generally limited, with only one study [44] analyzing a comprehensive set of 22 criteria. Lastly, only a few studies [42; 47] employed objective measures to validate the reliability of AI-generated exercise plans. The AI-generated programs covered various exercise domains, including core exercises [45], strength training [46; 48] running plans [44], calisthenics [42], weight loss exercises [47], and rehabilitation [43; 49; 50; 51]. Overall, the findings suggest that AI-generated exercise programs align with general exercise science principles but often lack specificity, progression, and adaptability.