Traumatic liver injury （TLI） is one of the most common and life-threatening visceral injuries in blunt abdominal trauma. Early and accurate assessment of the extent and severity of liver injury is crucial for selecting appropriate treatment strategies and improving patient outcomes. Currently，computed tomography （CT） remains the most reliable imaging modality for evaluating liver injuries. However，CT interpretation is highly dependent on radiologists' expertise and is associated with challenges such as subjectivity，time-consuming image review，and an increased risk of missed diagnoses under heavy workloads. In recent years，deep learning has achieved remarkable progress in medical image analysis. This review explores a precision assessment framework for TLI based on injury detection，quantitative severity assessment，and identification of associated imaging findings. Furthermore，it discusses how diagnostic and therapeutic decision-making can be enhanced through imaging optimization and multimodal data integration. The challenges facing the clinical application of deep learning models，including the long-tail effect of medical data，the “black-box” nature of algorithms，and integration into clinical workflows，are also analyzed. Finally，future research directions，such as explainable artificial intelligence and edge computing，are proposed to promote the standardized application of artificial intelligence technologies in the diagnosis and management of TLI.