The integration of computational advances in microscopy has enhanced our ability to visualise immunological events at scales. However, data generated with these techniques is often complex, multi-dimensional, and multi-modal. Data science and artificial intelligence (AI) play a key role in untangling the wealth of information hidden in microscopy data by enhancing image processing, automating image analysis, and assisting in interpreting the results. With this Review, we aim to inform the reader about the advances in the fields of fluorescence and electron microscopy with a focus on their applications to immunology and virology, and the AI approaches to aid image acquisition, analysis, and data interpretation. We also outline the open-source tools for image acquisition and analysis and how these tools can be programmed for an image-informed, AI-assisted acquisition.
Morone et al. (2025) studied this question.