The present laboratories appear to be extremely advanced to a decade back, yet shouldn’t something be said about a decade from now?
The way research is carried out has been radically impacted by the advancement of technologies. The incorporation of robotics and automation has revolutionized procedures, transforming dreary manual processes into automated ones. The introduction of artificial intelligence to laboratories is greatly influencing factors like procedure efficiency, reproducibility, data collection and analysis, flexibility, cost-savings.
AI assists the researchers explore innovative ideas, such as a new pathway or target for a drug — and coupling this with predictive analysis for exceptional results. Such a practice could save millions of dollars in personnel and experimental costs, and result in a noteworthy probability of success.
There is an array of potential uses for artificial intelligence within the laboratory, and how they interact with humans and automated instruments. For example, Natural language processing (NLP) could be utilized to transcribe a researcher’s speech or enable instruments to start or stop tasks, instruments could self-calibrate if irregularities are detected, or report to users if steps in a process are missed, preparation of materials, the calibration of instruments, automated requests for the servicing of instruments and even the ordering of assays from external partners.
In the laboratory, artificial intelligence is more about augmented intelligence. That is, if data is recorded accurately, then new systems and programs can learn based on algorithms and training sets from any past statistics accessible in the system. It’s likewise why organizations should collect data from each examination, regardless of whether the outcomes aren’t what was normal or expected: all information can recount a story. When comprehensive sets of data are recorded, combined with content and any other relevant information, AI and automation in the lab can really start to break new grounds.
The sole factor limiting the ability of AI is the amount of data available. With access to global health data, systems would be able to see trends and suggest ways of resolving them before they even become serious issues, and draw conclusions on lifestyle and genetic conditions, offering more significant understanding into pre-emptive action.
As the world turns out to more and more connected, it is imperative that laboratories see the benefits of investing in these innovations to promote science.