In this talk, Alberto Bartesaghi, Associate Professor of Computer Science, Duke University, will describe his efforts to employ AI-based techniques for feature recognition to drive the development of strategies to accelerate structure determination using cryo-EM. For example, finding the conditions to stabilize a macromolecular target for imaging remains the most critical barrier to determining its structure. SmartScope is the first framework to streamline, standardize, and automate specimen evaluation using deep-learning-based object detection, allowing it to perform specimen screening in a fully automated manner. During the downstream data analysis, randomly distributed copies of the protein of interest need to be identified, extracted, and averaged in 3D to obtain a high-resolution structure. Existing neural-network-based detection algorithms require extensive labeling and are very slow to train. Leveraging positive unlabeled learning and consistency regularization, we propose a novel framework that can identify particles much faster than previously possible while still using very few labels. Altogether, these advances in AI-driven cryo-EM greatly facilitate and accelerate the structural analysis of important biomedical targets, thus lowering the barrier of adoption of this powerful technique for protein structure determination.