In this AI Podcast, Mark Michalski from the Massachusetts General Hospital Center for Clinical Data Science discusses how AI is being used to advance medicine.
“Medicine — particularly radiology and pathology — have become more data-driven. The Massachusetts General Hospital Center for Clinical Data Science — led by Mark Michalski — promises to accelerate that, using AI technologies to spot patterns that can improve the detection, diagnosis and treatment of diseases.”
Massachusetts General Hospital — which conducts the largest hospital-based research program in the United States, and is the top-ranked hospital on this year’s US News and World Report “Best Hospitals” list — recently established the MGH Clinical Data Science Center in Boston. The center will train a deep neural network using Mass General’s vast stores of phenotypic, genetics and imaging data. The hospital has a database containing some 10 billion medical images.
To process this massive amount of data, the center will deploy the NVIDIA DGX-1 — a server designed for AI applications, launched earlier today at the GPU Technology Conference — and deep learning algorithms created by NVIDIA engineers and Mass General data scientists.
Deep learning is revolutionizing a wide range of scientific fields,” said Jen-Hsun Huang, CEO and co-founder, NVIDIA. “There could be no more important application of this new capability than improving patient care. This work will one day benefit millions of people by extending the capabilities of physicians with an incredibly powerful new tool.”
Using AI, physicians can compare a patient’s symptoms, tests and history with insight from a vast population of other patients. Initially, the MGH Clinical Data Science Center will focus on the fields of radiology and pathology — which are particularly rich in images and data — and then expand into genomics and electronic health records.
We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation,” said Dr. Keith J. Dreyer, vice chairman of Radiology at Mass General, associate professor of radiology at Harvard Medical School and executive director of the center. “Guided by precision healthcare, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations. Without the processing capabilities of GPUs, this would not be possible.”
Deep learning tools present a tremendous opportunity to improve healthcare. By increasing efficiency and accuracy of diagnostic testing, and elevating meaning from vast troves of clinical data, deep learning provides a pathway to true precision care. However, there are challenges in the translation of this technology to the clinic: model performance, infrastructure development, data privacy, hospital policy, and vendor relationships are all critical components to this effort. We’ll discuss the early experience of the MGH & BWH Center for Clinical Data Science in supporting the translation of deep learning technologies in medicine, touching upon many of the existing and emerging technical, clinical, and cultural challenges that this work presents.