Microscopes that think: AI-powered imaging cuts data collection time in half
Researchers have mapped a framework for "smart microscopes" that adjust focus, lighting, and capture speed in real time using artificial intelligence—eliminating wasted data and speeding up drug discovery and disease research. The approach could accelerate time-to-market for biotech firms and reduce costs in clinical diagnostics by automating optimization tasks that now require expert judgment.
Originaltitel: Smart microscopy: adaptive microscope control to improve the way we see life
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.