In this study, the effectiveness of three commercially available AI algorithms (BoneView, Rayvolve, and RBfracture) was evaluated for bone radiograph interpretation. Two radiologists independently interpreted 2,926 radiographs, first unaided and, after a 14-day control period, with each AI tool in a randomized crossover design. The ground truth was established by computed tomography (CT) scans. After comparison, sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), interpretation time, diagnostic confidence (5-point Likert), and rates of additional CT recommendations and senior consultations were determined. Although AI assistance did not significantly change performance for fractures, dislocations, or effusions, median interpretation times decreased with AI (Reader 1: 34 s to 21–25 s; Reader 2: 30 s to 21–26 s; all p < 0.001), and confidence improved across all tools; (Reader 1: 449 to 509, p < 0.001; Reader 2: 439 to 483, p < 0.001), (Reader 1: 449 to 456, p = 0.029; Reader 2: 439 to 449, p < 0.001), and (Reader 1: 449 to 457, p = 0.017; Reader 2: 439 to 448, p = 0.001) for BoneView, Rayvolve, and RBfracture respectively. The authors note that data were collected at a single center, which may introduce systematic bias, confounding variables, and limit generalizability to other clinical settings.