As the advancement in optical microscopy has pushed our understanding about life science towards finer, deeper, and faster, the complexity of instrumentation, data, and analysis has dramatically increased. Over the past decade, the role of optical microscope has transferred from a descriptive tool to a quantitative approach; the quality and complexity of large-volume images generated from these state-of-art microscopes preclude conventional visual and manual analysis. Our group has recently integrated computer vision, neural networking, and machine learning into the image processing and analysis, and has developed a quantitative bioimage analysis software specifically for the live-cell image data. The first half of this presentation will describe the technology advancement of live-cell image analysis; particularly, I will discuss the utilization of artificial intelligence (AI) to improve the spatial and temporal resolution of single molecule imaging. In the second half, I will introduce our applications on chromatin motion and DNA damage. We expect that the exploration of the spatiotemporal dynamics in live cells will facilitate the diagnosis, treatment, and prevention of cancers or other diseases.