Machine Learning is Disrupting Life Science Research – For Good
Discussions seem to be popping up everywhere from industry events to articles in mainstream business magazines about the future of medicine and whether artificial intelligence (AI) and machine learning will displace the work being done by researchers and doctors. A recent interview in The New Yorker even suggested that radiologist training should be halted, since deep learning will be doing a better job than professionals within the next five years. While it’s true that artificial intelligence and computer-based algorithms are making their way into both the lab and clinical practice, the adoption of these new technologies will not replace the work of the researchers themselves. On the contrary: it’ll enable them to become more effective than ever before.
Big data is getting bigger by the minute. Preclinical researchers are focused on developing new hypotheses and ideas that can eventually translate into testing and deployment, which includes gathering an enormous amount of data, understanding and connecting the dots of different pathways, and coming to meaningful conclusions. There is also increasing demand, particularly in oncology, for better prognostic tests and companion diagnostics to inform treatment. Researchers are thus tasked with analyzing overwhelming amounts of data to identify biomarkers and develop robust assays. This is essentially like searching for a needle in a haystack, and is an incredibly time-intensive and challenging process.
Simply put, today’s machines are capable of crunching vast amounts of data and identifying patterns that humans cannot. AI and machine learning thus provides significant opportunities for life sciences research. Essentially, if you take enormous computing power and feed it tremendous amounts of data (e.g., from published research in scientific journals, patient records or other data sets), you get an artificial intelligence network that researchers can interact with in their daily work in a useful way. Researchers can use the network for data testing, plausibility assessments or to come up with new pathway interactions. They can detect biomarkers faster – those needles in the haystack – because the AI network makes it possible to understand what they look like and where to look for them.
AI also supports quality control, enabling researchers to better determine if a discovery is a rare event or whether it has real meaning and can be validated and reproduced. For example, in preclinical drug or test development, AI allows the researcher or the community to bring different information or cohorts together for big data sharing and collaboration, and more effective data mining. AI and machine learning programs can also train computers to decrease their errors rates over time, based on information gathered and mistakes made, while human errors rates essentially stay the same.
For years the industry has been accumulating immense amounts of data, and while advances in cloud technologies have provided a way to store all of that data, with AI and machine learning we now finally have a way to effectively utilize all the data that has been, and is being collected.
Despite all of the potential upsides, there is a lot of anxiety that AI will make jobs across the industry obsolete.
