[Frontiers and Perspectives of Artificial Intelligence in Forensic Research on CT Imaging Diagnosis of Rib Fractures]

Rib fractures are relatively common in thoracic trauma, and the number of fractured ribs as well as the presence of malunion are closely associated with criminal sentencing and civil compensation, thereby holding significant forensic significance. At present, the diagnosis of rib fractures in forensic practice primarily relies on computed tomography (CT) examinations. However, achieving accurate results often demands substantial time and effort from experienced forensic experts, with limitations in diagnostic efficiency and consistency. In recent years, the rapid advancement of artificial intelligence (AI) has led to the emergence of multiple AI-assisted imaging diagnostic models with practical applicability, effectively improving the efficiency and accuracy of rib fracture identification. This review systematically summarizes the latest application progress of AI in CT diagnosis of rib fractures, with a particular focus on recent advances in automatic detection, classification, localization, multitask integration, and fracture age estimation. By synthesizing existing literature and methodologies, this work aims to reveal the potential of AI in improving diagnostic efficiency and accuracy, and to provide a reference for the future development of intelligent diagnostic models with greater forensic utility.