Ash Kamath, senior director – Americas, EMEA & CIS and Dipanjan Paul, marketing manager, ACG Inspection look at the benefits of AI-powered inspection.
Pharmaceutical manufacturing is undergoing a fundamental shift towards greater efficiency, robust quality assurance and real-time defect detection. Visual Quality Inspection (VQI) is a critical part of this transformation, helping manufacturers verify that products meet predefined quality standards. Over the years, conventional image-processing-based inspection has provided a proven foundation for automated quality control. However, many such systems depend on predefined algorithms, parameterisation and operator intervention, particularly for critical settings such as inspection thresholds. This can introduce variability, increase dependence on skilled personnel and make complex, changing inspection requirements harder to manage.
Artificial Intelligence (AI), and particularly deep learning, offers a new approach. Instead of relying solely on manually defined rules, AI models can learn visual patterns from representative datasets and support robust defect and anomaly detection across complex use cases. This article examines the evolution from conventional image processing to AI-powered VQI, the role of deep learning, the business value of AI-enabled inspection, and how these technologies are being applied across pharmaceutical manufacturing.
Visual Quality Inspection is an essential component of quality control in pharmaceutical manufacturing. It involves assessing products and packaging for visual attributes such as colour, shape, dimensions, surface defects, deformations and printed information. The objective is straightforward: identify defects and deviations consistently before they progress further through the production or supply chain.
Conventional computer-vision solutions have played an important role in automating this task. Typically, traditional image-processing systems use predefined algorithms for image preprocessing, followed by segmentation, feature extraction and interpretation. These methods have a proven track record and remain valuable for well-defined inspection applications. However, their performance can depend significantly on how the system is configured and maintained. On the production line, operators may need to be appropriately trained to manage critical parameters such as threshold settings. As inspection requirements become more complex and the number of product variants and defect types increases, the limitations of a highly rule-based approach become more apparent.
AI-powered inspection addresses this challenge by introducing data-driven learning into the inspection process. Deep learning models can be trained on representative datasets and retrained for specific use cases, enabling greater flexibility in recognising complex visual patterns. The result is a shift from inspection systems that primarily follow predefined rules towards systems that can learn from data and continuously improve their ability to identify relevant anomalies.
