Artificial intelligence for microscopy: from image analysis to intelligent imaging

Abstract

Recent advances in microscopy are generating unprecedented volumes of high-resolution biological data across spatial and temporal scales. Artificial intelligence is rapidly transforming how this data is interpreted, enabling the automated extraction of quantitative information from complex images and opening new opportunities for data-driven discovery in biology and medicine. This lecture will discuss recent progress in the integration of deep learning with microscopy workflows, highlighting how AI methods are reshaping the analysis of biological images and enabling reproducible, scalable computational pipelines. Particular emphasis will be placed on the development of accessible AI tools and open computational frameworks that allow researchers to deploy advanced machine-learning models directly within established bioimaging environments. Interdisciplinary approaches that combine AI-based image analysis with physical and biological modeling are also emerging as powerful strategies to study complex biological systems. Such approaches enable quantitative investigation of cell mechanics, tissue organization, and large-scale three-dimensional histological reconstruction, illustrating how computational methods can reveal new insights into biological structure and function across scales. Finally, emerging directions at the intersection of artificial intelligence and optical imaging will be discussed, including smart microscopy systems capable of adaptive acquisition, agent-based AI approaches for scientific discovery, and the growing importance of sustainable computing in data-intensive bioimaging.