Using AI in a heavily regulated environment: drug and vaccine inspection

#News(IoTStack) [ via IoTGroup ]

As data collection techniques become more efficient and widely available many pharmaceutical companies are starting to consider Artificial Intelligence (AI) for improving their drug and vaccine inspections.Machine Learning (ML) a subsection of AI takes advantage of this improved data collection to create algorithms that can detect defects in real-time with human-level accuracy.

Currently pharmaceutical companies do drug and vaccine inspections using two approaches: manual visual inspection by human operators and classic Computer Vision (CV) algorithms.AI algorithms should be considered instead of manual inspection because they eliminate the chance of human error thus providing more consistent and accurate results.At the same time since using these types of algorithms reduces the number of workers involved in inspection the remaining workers can focus on other tasks which also secures labor costs for the company.

They employ a more rigorous mathematical approach whereas ML algorithms can be applied to any situation where there is available data and are therefore more suitable for visual inspections.AI algorithms process data in raw form which in the case of drug inspection includes images.Interestingly the number of companies actually implementing AI in inspections is drastically smaller than of those willing to apply it.For creating a well-functioning AI process it is necessary to have a team with people possessing both the knowledge of IT development and machine learning science as well as quality inspection and pharmaceutical regulations for instance.This is why tech companies that provide AI for drug inspections emerge.

This often simplifies the change management process at the corporation because they can co-work with a team of a small company that already has a solution for them and can help them with introducing it at the workplace.It is proved that the visual inspections with AI reach up to 90% precision demonstrating some room for improvement.Only

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