Production of medications requires maximum accuracy and regulatory compliance. Each batch must pass the set requirements because even slight deviation may lead to the recall of the medication or even non-compliance with the requirements.
For many years, rule-based systems were used for quality control of manufactured drugs. But with the use of AI in pharmaceuticals, this process is changing.
What Is Quality Control Based on AI?
Quality control based on AI uses machine learning and computer vision technologies to monitor production data, images, and sensory information in real-time mode.
Among other factors in pharmaceutical manufacturing, such aspects are considered:
- Inspection of pills and capsules
- Checking packaging of medications
- Checking accuracy of labels
- Monitoring fill levels of vials and syringes
- Detecting contamination
- Analyzing seal integrity
Unlike rule-based systems, AI technology can learn from the data and find defects that are hard to notice by humans or fixed-threshold systems.
How Does AI Improve Inspection Accuracy?
The AI system learns about the examples of good and bad product. With time, the system starts recognizing patterns related to the quality problem.
For example, computer vision can check the following defects of tablets in production process:
- Cracks on surface
- Color defects
- Irregular shape
- Chips or breakage
Also, the system can ensure proper packing of products: checking whether the label is in right place, visible and in accordance with standards.
One of the key benefits of AI in this case is that AI does not suffer from fatigue unlike human inspector.
Role of Industrial Internet of Things
AI systems of quality control use Industrial Internet of Things (IIoT) for collecting the data in real-time. It means that cameras and sensors collect the data from production line and feed it into AI system.
Process:
Production Line → Sensors & Cameras → Data Platform → AI → Defects Recognition
This will facilitate quality control in real time instead of post-manufacturing quality control at the end of the line.
Advantages
- Greater accuracy of detection
- Consistency improvement
- Human error reduction
- Faster production process
- Regulatory compliance
Challenges
- Data quality and labeling
- Model validation
- False positives and negatives
- System integration
- Regulatory documentation
- Cybersecurity
- Explainability of AI
Conclusion
AI-driven quality control is transforming pharmaceutical manufacturing through greater accuracy and efficiency. It augments the skills of humans but does not replace them.
To know more, click on PharmaFlux AI.
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