How can data science be used in lab automation?
Data science enhances lab automation by enabling centrally-integrated, cloud-based systems to collect and analyze operational data using AI and machine learning, allowing automated lab equipment to autonomously optimize procedures, detect errors, and collaborate across multiple labs for improved efficiency and minimal human oversight.
Most research and pharmaceutical laboratories have largely adopted lab automation, removing the need for human input, especially in repetitive tasks and routines. With the integration of data science in lab automation, these automation systems have become 'intelligent.'
Automated systems in labs, each tasked with performing particular routines, can be controlled from a centrally-integrated system in the laboratory. When each independent system generates data from its operations, the data is stored in a cloud-based platform. Algorithms, such as those of AI and machine learning, are then applied to the big data to derive insights. The centrally-integrated systems can then utilize these insights to make the automation systems more efficient. The automated systems can even develop novel protocols for effectively performing lab operations, mimicking human cognitive capabilities.
When data science is integrated into lab automation, the automated systems evolve from mundane to intelligent. The intelligent lab systems can autonomously readjust operations when lab results are unsatisfactory, halt routine procedures when a mistake occurs, or call for human intervention when a lab machine is performing sub-optimally. The lab can even be left to operate overnight with only minimal human oversight.
The impact of data science analytics is even more powerful when several laboratories combine their data in one cloud-based system and utilize data management tools. These different laboratories can have a protocol for standardizing and annotating their data before sending it to the cloud. Such collaboration from different automated labs results in even larger data sets for modeling with AI and machine learning algorithms. The bigger and more diverse the data sets, the greater the veracity of insights gathered from the analytics.
Ultimately, lab automation relieves human personnel in laboratories from performing repetitively mundane tasks and shifts their focus towards tasks that require critical thinking.
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