  Optical microscopy is an indispensable tool in life sciences research, but
conventional techniques require compromises between imaging parameters like
speed, resolution, field-of-view, and phototoxicity. To overcome these
limitations, data-driven microscopes incorporate feedback loops between data
acquisition and analysis. This review overviews how machine learning enables
automated image analysis to optimise microscopy in real-time. We first
introduce key data-driven microscopy concepts and machine learning methods
relevant to microscopy image analysis. Subsequently, we highlight pioneering
works and recent advances in integrating machine learning into microscopy
acquisition workflows, including optimising illumination, switching modalities
and acquisition rates, and triggering targeted experiments. We then discuss the
remaining challenges and future outlook. Overall, intelligent microscopes that
can sense, analyse, and adapt promise to transform optical imaging by opening
new experimental possibilities.
