New sensor technologies offer greater accuracy than ever when verifying sterility in pharmaceutical environments. However, issues like calibration drift stand in the way of that reliability. Even the most sophisticated monitoring tools can slowly become less precise over time. Calibration is the obvious solution, but it is not a foolproof process, so errors are still possible. Artificial intelligence (AI) may provide a better way forward.
By Emily Newton
The Problem With Conventional Calibration
Just as sterility monitoring is only as reliable as its sensors are accurate, calibration is only helpful when it is both comprehensive and precise. That leaves considerable room for human error.
Calibration mistakes and other human errors are a common cause of equipment downtime in health care and pharmaceuticals. It is easy not to calibrate a sensor along its entire range or to adjust a machine incorrectly according to test results. Maintaining utmost care takes a lot of time and focus — two things which are typically not humans’ strong suit.
Manual testing and adjustment can also be slow, which creates two issues. First, it means extended machine downtime, leading to lost productivity. Second, it means the employees performing this work may get tired, overloaded and distracted, raising the risk of human error all the more.
In light of these shortcomings, it should be no surprise that calibration drift may be more common than companies realize. Thankfully, there is another way.
Where AI Comes In
Like many pharmaceutical processes, calibration drift tests can benefit heavily from automation. AI-enabled calibration automation is becoming increasingly commonplace, and it is unlocking new standards of sterility assurance in several areas.
Automated Calibration Drift Tests
At its most simplistic, AI can automate the same kinds of sensor tests a human would normally perform. This has two main advantages — accuracy and efficiency.
Humans are infamously prone to mistakes when taking on repetitive, data-heavy work. Pharma entities lose millions of dollars annually because of these errors, but the workflows people struggle with are typically where AI is most reliable. Consequently, a sensor array that detects and corrects its own calibration drift will make more accurate adjustments than a manual process.
Automated testing and tweaking also mean lab staff do not need to take time out of their busy days to perform such work. The equipment will correct sensor accuracy issues as soon as they are measurable, so labs avoid scheduling complications, too.
Adaptive Calibration
AI-driven sensor calibration can also adapt over time. As machine learning models get new information, they can adjust their approaches to account for larger trends. That way, auto-calibrating systems can manage things like wear, temperature fluctuations and more, which may cause conventional strategies’ dependability to vary over time.
These ongoing improvements are especially valuable when dealing with equipment with broad factory calibration settings. Expert calibration can make tools more accurate, turning cheaper machinery into top-of-the-line assets. In addition to preventing sterility reading errors, this can save money on lab equipment.
Predicting Calibration Drift
Some AI models can even predict calibration drift before it happens. Predictive analytics models will learn how frequently sensors need calibration and what events lead to it as they monitor the system. They can then recognize when adjustments will be necessary in the future for more proactive steps.
It is the same underlying concept as predictive maintenance, which can reduce downtime by 15% and has become popular among manufacturers. Instead of predicting breakdowns, though, the model detects the risk of calibration drift before it occurs so it can recalibrate sensors early and prevent errors entirely.
Eliminating calibration errors means sterility monitoring tools remain as accurate as possible throughout their useful service lives. That presents massive savings potential and makes regulatory compliance much easier.
Remaining Obstacles With AI Calibration
AI-driven calibration is too promising to ignore. At the same time, it is easy to get stuck in the same trap with AI as the one leading to undetected calibration drift in the first place. No technology is perfect, so over-relying on it is a sure path toward significant issues.
Most notably, AI needs a lot of high-quality data to be reliable. This data demand can result in lengthy, expensive model training processes, which may weaken some of the calibration’s cost-saving impacts. Even the most reliable machine learning models can still hallucinate, too, so experts must verify the work periodically to ensure everything stays on track.
Some sterility sensor arrays are also not the most computationally complex systems. While that is good news for affordability and ease of use, it also means not all equipment has the hardware to support an advanced AI model. Consequently, implementing self-calibrating models can entail some costly upgrades. The resulting savings should compensate for the investment over time, but the initial expense remains a barrier for some labs.
AI Calibration Can Unlock New Standards of Sterility Assurance
While it may not be perfect, AI calibration shows a lot of promise. Labs that invest in it now and account for its shortcomings could make their sterility assurance processes more efficient, accurate and reliable than ever. AI is not a panacea, but it is a big step in the right direction.
Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)
.jpeg)
