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.
Step 2: Improving drug trial efficiency
Artificial intelligence can help improve clinical trials is through the development of protocols. Using artificial intelligence applications, such as machine learning, you can process more real-world data. This allows a researcher to get access to more comprehensive and accurate data from a trial and thus improves the trial effectiveness.
Step 3: Identifying treatments
At UCL in London, they are using machine learning to improve clinical trials on new stroke medicines and predict how they will react to treatment using signals that they were previously unable to detect. In addition to this, they are also able to get a much deeper understanding of the different types of strokes. This helps them to predict which drugs will be more suitable to treat the patient.
This application of artificial intelligence in medicine can be used for many different conditions to deepen our understanding and develop treatments of many complex diseases, including cancers. How will these developments change medicine over the next few years? With ever more people becoming trained in and starting to experiment with artificial intelligence, it is clear that there is great potential to increase our understanding of the human body and its chemistry.
With our healthcare systems facing a great number of complex challenges including complex diseases like Alzheimer’s, greater demands on doctors with limited funding and an ever increasing number of antibiotic resistant superbugs, artificial intelligence in medicine could be our greatest weapon in the fight against disease.