This systematic review and meta-analysis evaluates the efficacy of Artificial Intelligence (AI) in detecting fractures using various imaging modalities and data types. The study identified 66 eligible studies, with 54 using imaging-related data, 9 using tabular data, and 3 using both. Vertebral fractures were the most common outcome, followed by hip fractures. Hip fractures exhibited the highest pooled sensitivity (92%) and specificity (90%). Radiographs demonstrated the highest sensitivity (94%) and specificity (92%). AI models achieved high accuracy, particularly with radiograph images, but significant flaws in study design and reporting limit real-world applicability. The study highlights the potential of AI in fracture detection but emphasizes the need for enhanced transparency and adherence to standardized guidelines to improve clinical applicability.This systematic review and meta-analysis evaluates the efficacy of Artificial Intelligence (AI) in detecting fractures using various imaging modalities and data types. The study identified 66 eligible studies, with 54 using imaging-related data, 9 using tabular data, and 3 using both. Vertebral fractures were the most common outcome, followed by hip fractures. Hip fractures exhibited the highest pooled sensitivity (92%) and specificity (90%). Radiographs demonstrated the highest sensitivity (94%) and specificity (92%). AI models achieved high accuracy, particularly with radiograph images, but significant flaws in study design and reporting limit real-world applicability. The study highlights the potential of AI in fracture detection but emphasizes the need for enhanced transparency and adherence to standardized guidelines to improve clinical applicability.