This study, authored by Lex et al., evaluates the effectiveness of artificial intelligenc (AI) models in diagnosing hip fractures and predicting postoperative outcomes relative to traditional methods. The authors performed a systematic review and meta-analysis of 39 studies involving 39,598 plain radiographs and 714,939 hip fractures. Diagnostic accuracy was assessed by comparing AI models to clinician performance, and predictive accuracy for outcomes like mortality and hospital stay was analyzed against traditional statistical techniques Findings showed AI models had a sensitivity of 89.3%, specificity of 87.5%, and a mea area under the curve (AUC) of 0.84 for mortality prediction, comparable to traditional methods. The study concluded that while AI is promising for diagnosing fractures, its advantage over traditional statistical models for outcome prediction remains limited