From Conventional Image Processing to Artificial Intelligence in Electron Microscopy: Toward Robust, Automated, and Enhanced Materials Characterization Through Insights from Medical and Biological Imaging

Electron microscopy is fundamental to modern materials characterization; however, the quantitative analysis of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) data remains challenged by noise, imaging artifacts, acquisition variability, complex microstructures, and operator-dependent interpretation. This review examines the evolution of computational analysis from conventional image-processing techniques, including filtering, deconvolution, enhancement, and segmentation, toward machine learning (ML) and deep learning (DL) approaches for electron microscopy. Particular attention is given to convolutional and generative architectures, self-supervised learning, transfer learning and domain adaptation, transformers and foundation models, physics-informed learning, and multimodal integration of imaging, spectroscopic, diffraction, and three-dimensional data. Advances in medical and biological imaging are further examined to identify transferable strategies for data-efficient learning, image reconstruction, multimodal analysis, uncertainty quantification, and external validation. Collectively, these developments indicate a transition from task-specific image processing toward increasingly integrated, automated, and quantitative characterization workflows. Nevertheless, limited datasets, domain shifts across instruments and material classes, computational demands, physical consistency, and insufficient standardization remain major challenges. Future advances will require robust, generalizable, and physically informed artificial intelligence frameworks capable of integrating heterogeneous microscopy data while maintaining scientific interpretability and reliability.