Fiber optic imaging technology can achieve high-resolution imaging in narrow areas due to the small size and flexibility of optical fibers. Fiber optic imaging can also be employed in biomedical research and industrial inspections. However, there are bottleneck problems in multi-core and multi-mode fiber imaging systems, limiting their resolution and accuracy. This paper briefly introduces representative research on the applications of deep learning to address these bottleneck problems in various fiber imaging modalities such as fluorescence imaging, quantitative phase imaging, speckle imaging, and multispectral imaging. Existing bottleneck in this interdisciplinary research field involving deep learning and fiber optic imaging are also discussed. Additionally, we envision the broad application prospects of intelligent fiber optic imaging systems.