Exploring the Current Role of Deep Learning in Radiologic Imaging of Gastrointestinal Diseases

Abstract

ABSTRACT Considering the nonspecific nature of gastrointestinal complaints and the broad differentials of gastrointestinal symptomatology, imaging plays a vital role in the formulation of diagnoses. As a result, artificial intelligence (AI) tools have emerged to assist radiologists in the interpretation of gastrointestinal imaging and to mitigate diagnostic errors. Among the main subtypes of AI applied in this field is deep learning (DL), a subfield of machine learning (ML) that uses artificial neural networks to analyze data and has proven to be superior to traditional ML methods in radiologic imaging analysis. In this review, we discuss DL applications in gastrointestinal imaging across different modalities, including x-ray imaging, ultrasonography, computed tomography, magnetic resonance tomography, and positron emission tomography. Moreover, we outline the challenges and ethical considerations facing the growing role of AI in clinical practice.

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