Acute care surgery consists of three divisions, including trauma surgery, emergency surgery, and surgical critical care. In recent years, imaging has progressively improved with the introduction of artificial intelligence and hybrid techniques, ranging from X-rays to sonograms to laparoscopies. In artificial intelligence, the development of machine learning is a collaborative effort of various fields, including mathematics, physics, and engineering. Radiomics is an emerging field of research surrounding medical imaging involving artificial intelligence (AI) that benefits greatly in emergency surgery. The use of AI has also begun to be greatly involved in image-guided surgery and hybrid imaging. Image-guided surgery refers to any invasive intervention performed with the assistance of images of the targeted organ. Imaging modalities include endoscopy, fluoroscopy, intraoperative computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound, and stereotaxis. Image-guidance routinely identifies blood vessels, lymph nodes, bony structures, and macroscopic tumor lesions. Hybrid imaging, with the combination of the above, explores how AI significantly enhances diagnostics and navigation in emergency general surgery through data handling, image reconstruction, and surgical guidance. Understanding the basic principles of hybrid imaging, functionality, information processing, and utilization is crucial to appreciating the implementation of AI in this field and the promising future it holds for medical imaging. This chapter is an introduction to the principles listed and the revolution of AI in advancing the field of emergency surgery via the progress of image interpretation.