<p>Artificial intelligence (AI)-assisted fracture detection on conventional radiographs has evolved over recent years from an experimental approach into a&#xa0;clinically available application. In many anatomical regions, such systems already show high diagnostic accuracy. However, diagnostic performance alone does not necessarily translate into improved patient care. Current evidence is still based predominantly on retrospective studies and, therefore, provides only limited insight into the effects on clinical workflows, diagnostic decision-making, and patient-relevant outcomes. Beyond fracture detection itself, another aspect is increasingly coming into focus: the potential value of reliable fracture exclusion. This may represent an important clinical benefit by reducing unnecessary follow-up imaging, lowering radiation exposure, and improving the allocation of diagnostic resources. Such benefits, however, depend on responsible use in everyday practice, including technical integration, user training, and a&#xa0;realistic understanding of the limitations of these systems. AI-assisted fracture detection, thus, illustrates a&#xa0;broader principle in medical AI: the value of an algorithm should not be judged solely by diagnostic accuracy, but by its actual contribution to patient care. As one of the more mature application areas, it may also help define standards for the introduction, evaluation, and regulation of future AI systems in medicine.</p>