Advances and Challenges in Machine Learning‐based Image Analysis for Monitoring and Predicting Organic Crystal Formation

Manual crystallization experiments have always been challenging, requiring extensive process development expertise and often resulting in unpredictable results. The crystallization process plays a critical role in the development of high‐quality organic materials, which are essential for various industries such as pharmaceuticals, materials science, and electronics. Therefore, crystallization experiments are in urgent need of innovative methods to ensure consistency, efficiency, and scalability. Recent studies have shown that machine learning can effectively assist crystal detection and segmentation, thus providing a new way to optimize organic crystallization processes, improving both the speed and precision of crystal formation. However, a comprehensive review of machine learning‐based approaches for organic crystallization process monitoring remains elusive. It is therefore necessary to review the machine learning technologies involved, their current applications, technical challenges, and development blueprints. In this work, we focus on the application scenarios, basic principles, and common tools of machine learning methods based on image detection and segmentation in effectively monitoring the crystallization process of organic crystals, especially the research on artificial intelligence technology in the detection of crystal size and morphology, monitoring, and optimization of crystallization processes. Through this work, we aim to provide the oretical references and practical guidance for researchers in related fields.