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Artificial Intelligence: the best medicine for French pharmaceutical industries

by | Feb 8, 2019 | Business

Visuel Pharmaceutique

Major French pharmaceutical groups are increasingly equipping themselves with Artificial Intelligence technology in order to modernize and develop their activities – as Sanofi and Pierre Fabre have recently demonstrated. Sanofi, the French giant in the sector, announced last week that they plan to invest €60 million in new digital technologies by the end of 2021 to adapt and modernize their entire global industrial network, including 75 plants around the world. These new technologies will make it possible to monitor the entire production chain in real time or perform predictive maintenance using connected sensors and artificial intelligence systems, thus increasing their industrial competitiveness and the productivity of their employees.

 

For its part, the second biggest private pharmaceutical group Pierre Fabre organized an “AI Health Challenge” focused on the prevention of skin cancers during the 12th edition of the e-health summer university. In parallel with this “AI Health Challenge”, the group created by the end of 2018 a new European-wide Observatory of AI for healthcare professions with a view to establishing an accurate mapping of skills and assessing the penetration rate of AI among professionals and patients.  

 

These initiatives led by Sanofi and Pierre Fabre show how AI is becoming a major resource for the modernization and productivity of the pharmaceutical industries. A Siemens Financial Services study published at the World Industry Forum in Hanover estimates the potential gain from the digitization of production lines in the pharmaceutical sector at over €60 billion. In France, this transition would reduce costs between €1 billion and €2 billion. Equipping production lines with sensors, connected objects and computer systems would increase productivity and improve equipment planning, forecasting and maintenance with automatic alerting devices, and continuous monitoring of the generated data.  

 

These devices are mostly based on visual recognition, a rapidly growing branch of AI. This technology offers many opportunities for development in this sector, particularly through its optimization of production lines, laboratory experiments and safety.

 

As part of the optimization of production lines, visual recognition helps to set up a predictive maintenance and quality control system. Thanks to cameras installed on these production lines, it is possible to detect worn, damaged or defective parts on the products. For each anomaly, an alert is triggered, avoiding the current high downtime in pharmaceutical plants. According to an analyst in the Siemens study, digitization and data analysis could reduce these downtimes by 30% to 40%, thus significantly improving the overall efficiency of the equipment. Moreover, if the law now requires a double human verification of the condition and cleaning of tools in factories, a visual recognition system is perfectly capable of doing this verification task, allowing factories to save time and gain in productivity.

 

Concerning drug tests on animals in laboratories, the experimental periods are long and costly, but thanks to cameras equipped with a visual recognition system that continuously film the animals’ behavior, it is possible to identify abnormal behavior among these animals in real time. Today, observation is done manually, but this method could save pharmaceutical companies time and allow staff to focus on higher value-added tasks.

 

Finally, visual recognition meets the security challenges of the pharmaceutical industry. Indeed, research activities in laboratories, or production operations in factories, involve the handling of chemical and toxic products, with numerous risks of intoxication, skin, respiratory and digestive damage and various injuries. In order to protect themselves from these risks, operators must imperatively wear their personal protective equipment (masks, gloves, safety glasses), but this is not always the case. A visual recognition system can detect improperly worn or adjusted equipment, non-compliance with hygiene measures, and other potential risk factors such as a fire outbreak.

 

The growing interest of major French pharmaceutical groups in AI and the wide range of applications that visual recognition offers to this industry are very promising. At deepomatic, we recommend addressing the various issues that visual recognition can resolve in their entirety in order to optimize the solutions provided, create synergies between the various application areas, and finally, increase the productivity of companies.

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