Artificial intelligence (AI)-based reconstruction is a promising method for MRI reconstruction. However, deep neural networks may exhibit instabilities in conditions that are difficult to identify with patient images. The purpose of this work is to investigate whether digital phantoms can help evaluate the performance of AI-based MRI image reconstruction. We chose AUTOMAP as an example of AI-based reconstruction method, with the network being trained with 50,000 paired patches of T1W healthy brain images and corresponding noisy k-space data. We tested the network with noisy k-space brain images, digital phantom images, and hybrid images (i.e., brain test images that contained an inserted lesion-like object). The set of brain test images was used to evaluate the global reconstruction accuracy in terms of mean squared error (MSE). The digital phantoms were designed to test image homogeneity and resolution. The hybrid images were constructed to mimic unhealthy patient for testing whether the AI reconstruction model trained with all healthy brain images would yield equal performance on abnormal brain test images. We also selected two test cases (one brain and one phantom) to quantitatively compare AI-based reconstruction and IFFT in terms of local reconstruction accuracy, which was measured by mean intensity and homogeneity of a region of interest (ROI) in a range of noise levels.