Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Abstract

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

Keywords: abdominal radiology; artificial intelligence; colorectal; gastric; hepatic; imaging; machine learning; pancreatic.

Figure 1

Summary of AI applications in abdominal radiology.

Figure 2

Reported AUC values for AI model diagnostic accuracy of liver lesions by modality.

Figure 3

Reported AUC for AI model vs. radiologists for detection of lymph node metastasis from PDAC.

Figure 4

Comparing accuracies of a deep learning model to novice and expert endoscopists for IBD diagnosis.

Figure 5

Summary of challenges and limitations of AI in abdominal radiology.