Shoulder MRI is typically acquired with multiple number of signals averaged (NSA) in order to average out breathing motion artifacts. However, higher NSA leads to a longer scan time and patient discomfort. In this study, we investigated the use of a deep learning-based reconstruction algorithm to highly accelerate shoulder MRI. Adaptive-CS-Net, a deep neural network previously introduced at the 2019 fastMRI challenge, was expanded and presented here as a Compressed-SENSE Artificial Intelligence (CS-AI) reconstruction. The purpose of this study was to compare the image quality of shoulder MRI between reference and accelerated methods; SENSE, Compressed-SENSE, and CS-AI.