The Power of Data, the Power of Change
Commentary
GEORGE Q. DALEY
Biomedical scientists are riding a powerful wave of change in how we conduct our research.
Increasingly, biomedical science is being performed at the interface of wet-lab experimentation and high-powered computational and systems approaches. Artificial intelligence and machine-learning algorithms are becoming indispensable tools for virtually all forms of biomedical inquiry and are having profound effects on the practice of medicine.
This wave of change has been building for decades. In 1999, for example, the NIH’s Biomedical Information Science and Technology Initiative noted that researchers were spending less time in wet labs gathering data and more time working in teams to harness the resources of computational technologies. “Digital methodologies,” the initiative stated, “not just digital technology, are the hallmark of tomorrow’s biomedicine.”
In the Blavatnik Institute at HMS, we have acted on this foresight and are adding our own energy to the movement. We have a strong record of integrating quantitative and computational approaches in our research. In our Department of Biomedical Informatics, teams of researchers are using data-driven approaches to better decipher radiologic images and better determine gene-informed treatments for patients with diseases such as cancer. In the Department of Systems Biology, researchers are drawing insights from math, physics, and computer science to illuminate the behavior of rogue cells in cancer and infection.
Our researchers in the Departments of Health Care Policy and Global Health and Social Medicine also are embracing computational tools. Their use of artificial intelligence and deep learning will one day result in tools that allow us to better analyze and form policies that will improve medicine and the delivery of health care.
Artificial intelligence and machine-learning algorithms are having profound effects on the practice of medicine.
We are developing novel initiatives that will extend our exploration of computational science in biomedical research. For example, our new Theory in Biology Fellows program, led by mathematician and Professor of Systems Biology, Johan Paulsson, will bring together physicists and computational and data scientists, allowing their work to be the glue between different programs and different modes of intellectual inquiry, while our therapeutics initiative is emphasizing the use of technology platforms to support collaborative efforts among our scientists.
These are exciting times for research as computational technologies provide biomedical researchers with exciting—and much needed—new tools. All of us who are dedicated to discovering ways to improve the health of all people, stem disease, and deliver care widely and equitably should embrace the power these tools bring to our work.