Abstract Smart microscopy lies at the intersection of biology, optics, engineering, and computer science. Unlike traditional microscopes, smart systems actively adapt their acquisition settings in real time based on information extracted from the sample, allowing experiments to navigate competing demands such as resolution, speed and sample health. In this review, we present a practical framework for what makes a microscope “smart,” defining smart microscopy as the combination of real-time analysis, feedback control, and automated actuation. To guide implementation, we classify smart microscopy approaches by experimental goal (quality-, event-, target-, information- or outcome-driven) and discuss the corresponding strategies for analysis and control. Finally, we highlight key challenges and the growing role of community-driven efforts in making smart microscopy more accessible and widely adopted across the life sciences.
Rates et al. (2026) studied this question.