Smart microscopy uses real-time data and automation to make microscopes more intelligent and responsive. Instead of relying on fixed settings, the system adapts on the fly - changing parameters like resolution, zoom level, frame rate, and focus, based on the sample and what is being observed. This not only improves image quality but also reduces the need for manual intervention, making experiments more efficient and scalable.

At the Cellular Disease Models Lab, we work with organoids—miniaturised, lab-grown versions of human organs used to study diseases and test treatments. These experiments generate large volumes of high-resolution image data, which presents major challenges in storage, processing, and analysis. To address this, we are developing an automated workflow that connects the microscope to a cloud-based system. Using tools like Azure and Databricks, image data is stored, analysed using AI models, and then used to make decisions in real time - such as automatically refocusing the microscope on areas of interest.

This approach shows how cloud technologies, microservices, and AI can be used to automate and coordinate complex lab equipment. By integrating real-time feedback and orchestration, we are transforming the microscope from a passive tool into an active participant in the research process.