Beyond spectroscopy: Machine vision as the future of non-destructive testing in 3D-printed pharmaceuticals

3D Printing (3DP) has revolutionized personalized medicine by enabling the production of patient-specific dosage forms and adaptable drug delivery systems. However, despite these advances, 3DP presents significant challenges in quality control (QC), particularly as traditional methods may not be suitable for small-scale or on-demand manufacturing. To address these challenges, it is essential to develop flexible, precise, and efficient QC strategies that ensure product integrity. This review focuses on non-destructive spectroscopic- and imaging-based testing techniques that can bridge this gap. Techniques such as Near-Infrared Spectroscopy, Raman Spectroscopy, Terahertz Spectroscopy, Optical Coherence Tomography, and Hyperspectral Imaging provide robust alternatives to conventional QC approaches. Moreover, Machine Vision (MV) is emerging as a promising tool for real-time defect detection and visual inspection. When integrated with advanced algorithms and artificial intelligence, MV enables automated decision-making and defect identification in 3DP pharmaceuticals. Recent advancements in deep learning, especially through Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based models, have further enhanced the potential of MV in this domain, offering improved accuracy, speed, and adaptability under varying conditions. This review also includes a comparative evaluation of these techniques, highlighting the increasing importance of non-destructive tools, particularly MV, in supporting innovation and ensuring quality within an increasingly personalized pharmaceutical landscape. Furthermore, it also discusses existing gaps, regulatory challenges, and the need for standardization to ensure the safe and reliable integration of these technologies into pharmaceutical manufacturing workflows.