Life Sciences & AI

AI technology is being applied to test new medicines, allowing organizations to bring them to market sooner, and to analyze large and complex data sets that help discover new, more effective therapies and pharmaceutical drugs

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Areas

Diagnosis and Disease Identification

The biggest challenge in medicine is correct diagnosis and identification of diseases, which makes it priority one in machine learning development. A 2015 report indicates that in excess of 800 cancer treatments are in clinical trials. Knight Institute researcher Jeff Tyner stated in an interview that the problem was putting all the data together to get at what they can use.

Electronic Health Records

Support vector machines (technologies for sorting patient email queries) and optical character recognition (a technology for digitizing handwritten notes) are essential components of machine learning systems for document classification. Examples of these technologies are MathWorks’ MATLAB (a machine learning tool that has handwriting recognition applications) and Google’s Cloud Vision API.

Drug Discovery & Manufacture

Machine learning plays many roles in early-stage drug discovery, such as the development of new drug compounds, and in discovery technologies, such as next-generation sequencing. One of the first in this field is precision medicine, which makes identification of complex diseases and possible treatment modalities more efficient. The research uses unsupervised learning, which seeks data patterns without predicting outcomes.

Personalized Medicine

There is much research going on regarding the use of machine learning and predictive analytics in customizing treatment to a person’s unique health history. If successful, this can result in optimized diagnoses and treatment protocols. Currently, the focus is on supervised learning where doctors can use genetic information and symptoms to narrow down diagnostic options or make an educated guess about a patient’s risk.

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Challenges ahead

Investors seem confident that machine learning and AI will advance the life sciences and healthcare, but technological hurdles remain. Data privacy, for example, is a major issue. The most useful information is often personal medical data, which is difficult to access.

A U.K. study, however, shows that 83% of participants are willing to share their data for research as long as they remain anonymous. Drug development regulations require transparent algorithms. In other words, people need to understand how machine learning works.

It is not easy to find people with pharma expertise who also have an expertise in artificial intelligence. At TechEmergence, we’ve taken the opportunity to address the life sciences/data science talent divide, and we suspect that it will remain a serious issue for the coming half decade at least.