Applications of Artificial Intelligence in Gastrointestinal Endoscopic Ultrasound: Current Developments, Limitations and Future Directions.

Simple Summary: Endoscopic ultrasound (EUS) is widely used to diagnose lesions in the gastrointestinal (GI) tract. Artificial intelligence (AI) holds the potential to enhance the diagnostic capability of diagnostic EUS. While in their experimental phase, several models have been developed to demonstrate the accuracy, sensitivity, and specificity of AI in EUS. These models show the application of AI to facilitate tissue diagnosis and endoscopic training. We provide a comprehensive review of the use of AI in EUS. Endoscopic ultrasound (EUS) effectively diagnoses malignant and pre-malignant gastrointestinal lesions. In the past few years, artificial intelligence (AI) has shown promising results in enhancing EUS sensitivity and accuracy, particularly for subepithelial lesions (SELs) like gastrointestinal stromal tumors (GISTs). Furthermore, AI models have shown high accuracy in predicting malignancy in gastric GISTs and distinguishing between benign and malignant intraductal papillary mucinous neoplasms (IPMNs). The utility of AI has also been applied to existing and emerging technologies involved in the performance and evaluation of EUS-guided biopsies. These advancements may improve training in EUS, allowing trainees to focus on technical skills and image interpretation. This review evaluates the current state of AI in EUS, covering imaging diagnosis, EUS-guided biopsies, and training advancements. It discusses early feasibility studies and recent developments, while also addressing the limitations and challenges. This article aims to review AI applications to EUS and its applications in clinical practice while addressing pitfalls and challenges. [ABSTRACT FROM AUTHOR]