Quality control is a crucial but inefficient process in most manufacturing applications. Medicine producers face even more challenges than most. Their quality standards are higher, but if production is too slow, it could limit access to potentially life-saving treatments. AI could turn things around for the industry.
As machine learning techniques have improved, more medical manufacturers have turned to AI to streamline and refine their quality assurance (QA). It’s easy to see why since AI’s QA benefits apply across the entire production timeline.
Speedier R&D
AI’s advantages in pharmaceutical quality control begin in the research and development (R&D) phase. Machine learning models can simulate drug interactions to reveal which compounds might be the most promising candidates for new medicines without time-consuming real-world tests.
This speed and accuracy allowed Moderna to synthesize and test over 1,000 mRNA strands a month when researching COVID-19 vaccine candidates. Conventional, manual methods could only produce 30 strands in the same time frame.
AI can streamline the clinical trial process after selecting an ideal drug candidate. It starts with machine learning predicting at-scale real-world outcomes based on lab tests. From there, AI models can also analyze demographic data to highlight ideal areas and populations to test a medicine for greater participation.
These AI applications lead to less time in the planning phase while improving R&D accuracy. As a result, pharmaceutical products reach higher quality standards from the start without taking more time.
Fast, Accurate Error Detection
AI offers a more efficient alternative to manual quality inspections in the production process. End-of-line QA checks typically create bottlenecks, as closely inspecting products is much slower than the manufacturing speed. That’s especially the case with pharmaceuticals, where processes like cryo grinding can produce particles just 10 micrometers or smaller, requiring highly precise inspections.
Machine vision can perform these inspections much faster than humans. They can identify defects immediately because they compare products to hard data on what passable items look like. Consequently, some AI quality inspection systems can analyze pharmaceuticals as quickly as production lines make them.
On top of being faster than humans, AI is also more accurate. Medicine QA checks are highly detail-oriented. Humans struggle to perform these tasks without mistakes, but AI delivers the same standard every time.
Minimizing Human Error in Production
AI also streamlines QA in pharmaceutical manufacturing by making the production process less error-prone. Just as machine vision minimizes mistakes in quality testing, similar AI applications prevent them in production.
Collaborative robots significantly improve assembly precision, and AI features like machine vision make them more adaptable. Consequently, automated machines can deliver that accuracy even when other conditions change. Human and machine-related errors decrease as a result.
AI can also analyze digital twins of production lines to highlight where errors occur. Some models can even suggest potential changes, helping pharma companies refine their workflows to make quality mistakes less likely.
These AI-driven improvements mean medicines are less likely to have flaws before reaching the final QA inspection. By preventing mistakes instead of simply identifying them, pharmaceutical manufacturers minimize time spent removing defective medicines or fixing errors. Their product quality and production efficiency increase as a result.
AI Could Revolutionize Pharmaceutical Manufacturing
Pharma manufacturers face increasing pressure to improve their throughput and QA as attention around public health issues grows. Doing that with entirely manual workflows is challenging. AI provides the accuracy and speed these companies need to meet both sides of this demand.
AI is already making waves in pharmaceutical manufacturing, especially in the R&D phases. As this trend continues, more pharma companies will catch on and implement this technology in their processes. Slowly, the entire industry will reach higher efficiency and quality standards, all thanks to AI.
