Stand-alone artificial intelligence (AI) performance examples: false-positive and false-negative radiographs. (A) Radiograph shows a small corticated ossific fragment adjacent to inferior glenoid margin (arrow), likely sequela of prior trauma (chronic fracture) or calcified detached inferior labrum rather than acute fracture. AI noted this as an acute fracture using the DOUBT-FRACT threshold. Fifteen readers read this as acute fracture without AI. Four readers thought the fracture was chronic without using AI, but reversed their reading with AI. Only two radiologists, one rheumatologist, and two family medicine physicians recognized the chronicity of the fracture with and without AI. (B) Radiograph shows a subtle nondisplaced fracture of the fifth metacarpal base (arrow), which was not detected by AI. All readers missed this fracture with and without AI. Only ground truth readers noted the fracture. This fracture was only appreciable on the anteroposterior view shown here and was not clearly visible on (C) the oblique view or the lateral view (not shown) of the right hand. There were two predefined thresholds for fracture detection: high-sensitivity threshold named DOUBT-FRACT, equal to 50% after transformation, and high-specificity threshold named FRACT, equal to 90% after transformation [35].