From promise to practice: a scoping review of AI applications in abdominal radiology

AI is rapidly transforming abdominal radiology. This scoping review mapped current applications across segmentation, detection, classification, prediction, and workflow optimization based on 432 studies published between 2019 and 2024. Most studies focused on CT imaging, with fewer involving MRI, ultrasound, or X-ray. Segmentation models (e.g., U-Net) performed well in liver and pancreatic imaging (Dice coefficient 0.65–0.90). Classification models (e.g., ResNet, DenseNet) were commonly used for diagnostic labeling, with reported sensitivities ranging from 52 to 100% and specificities from 40.7 to 99%. A small number of studies employed true object detection models (e.g., YOLOv3, YOLOv7, Mask R-CNN) capable of spatial lesion localization, marking an emerging trend toward localization-based AI. Predictive models demonstrated AUCs between 0.62 and 0.99 but often lacked interpretability and external validation. Workflow optimization studies reported improved efficiency (e.g., reduced report turnaround and scan repetition), though standardized benchmarks were often missing. Major gaps identified include limited real-world validation, underuse of non-CT modalities, and unclear regulatory pathways. Successful clinical integration will require robust validation, practical implementation, and interdisciplinary collaboration.