By Claire Whittaker, CEO, Artificially Intelligent Claire.
In today’s world, we have access to more data than ever before. Analysis of big data is allowing us to solve ever more complicated problems at scale in a way that was not previously possible.
It’s a revolution.
Now it’s time to use the revolution of artificial intelligence in medicine. But what does this mean for the way we diagnose and develop new treatments?
Giving researchers access to pools of big data and equipping them with the tool of machine learning allows then a great opportunity to speed up processes that would have previously taken years. The typical timeline for new drug development from concept to market-ready product can range between 12 and 30 years. A significant portion of this time is devoted to research and development.
Patient diagnosis and treatment also experience, in particular for more acute diseases and ailments, a substantial amount of study to perfect. How can we leverage artificial intelligence in medicine, and its application in machine learning to help speed up this process?
We are starting to see the power of this technology to support medical diagnosis and treatment in several recent papers published by medical research institutes. These papers show the breadth at which the technology can be applied to increase the efficiency of the processes of diagnosis and treatment significantly. Thus allowing doctors to spend more time working directly with patients and saving institutions money.
Here we look at how artificial intelligence in medicine is driving forward our understanding of a variety of conditions. From drug development, through to improving the testing process and finally onto diagnosis — it is clear that there are multiple benefits of partnering with the technology.
Step 1: Drug Development
A recent article published in Nature highlighted several companies using machine learning to improve the drug development process. One company highlighted is Berg.
Berg is using machine learning to better understand and map human biology in far greater details. In their own words, “instead of hypothesizing the mechanism of a disease and focusing on only a few related compounds, we profile the entire disease by analyzing various patient biofluids (OMICS) and cell models (bio systems) as well as clinical information (EHRs).” This knowledge is then fed into Berg’s data analysis systems to be applied to drug development. The use of this artificial intelligence in medicine helps us to develop drugs more quickly and efficiently.