Imaging Advantage (IA), a U.S. platform provider of cloud based radiology service announced Friday a machine learning research initiative with faculty members from the Massachusetts Institute of Technology and Harvard Medical School/Massachusetts General Hospital, titled Singularity Healthcare.
Launching next quarter, Singularity is developing the artificial intelligence engine to be seamlessly incorporated into IA’s proprietary exam routing technology, to instantly pre-read digital x-rays and identify potential areas of injury and disease, while continuously learning from IA’s expanding database of 7 billion images. The algorithm will be applied before X-Ray images are routed to one of the 500 board certified radiologists connected in the cloud to IA’s platform.
Combining business and academic power, Singularity seeks to provide an applicable offering to problems central to the U.S. healthcare system. X-Ray exams constitute 50 percent of all radiology tests in healthcare, and radiology is the significant limiting factor in hospital emergency department patient flow and treatment.
The initiative brings together leading academicians from two renowned programs at MIT and Harvard. SP Kothari, PhD, Gordon Y Billard Professor of Management at MIT’s Sloan School of Management will lead the project, working in conjunction with Dr. Sanjay Saini, Professor of Radiology at Harvard Medical School and Vice Chairman of Radiology at Massachusetts General Hospital (MGH), who will advise on imaging quality and utility to radiologists, and Kalyan Veeramachaneni, PhD, Principal Research Scientist at MIT’s Institute for Data, Systems and Society.
“Inconsistency in testing and access to care contribute significantly to $1 trillion of waste in the $2.8 trillion U.S. healthcare industry,” said Brian Hall, Imaging Advantage’s president and COO. “If successful, Singularity will introduce a solution with potential to transform radiology by providing faster, more accurate and less expensive diagnostic testing, representing an indispensable innovation for radiologists.”
“The proposed deep-learning solution combines all layers of machine learning into a single pipeline, and then optimizes and meshes with other machine-learning algorithms on top of it,” said Dr. Kalyan Veeramachaneni. “Starting this endeavor with the enormous trove of meta data in Imaging Advantage’s archives, we can learn how decisions made at the initial, raw representation stage impact the final predicted accuracy efficacy.”
“We believe diagnostics is the gateway for the integration of artificial intelligence in healthcare,” said Hall. “Once we successfully develop this mechanism for X-Rays, we see the potential to expand the technology to CTs and MRIs, as well as other areas of time consuming diagnostic testing. The goal is to create a useful tool for radiologists, who are in shortage both domestically and internationally. Radiologists will continue to be indispensable.”
“Given the advances in the field of artificial intelligence that have taken place at MIT and elsewhere, and Imaging Advantage’s scale, we are not only optimistic about a successful outcome, but expect it to be realized on an accelerated schedule,” said Dr. Kothari.
In December, Imaging Advantage aligned with RadNet to collaborate and deploy models of delivering radiology services, combining the best-in-class outpatient expertise of RadNet with Imaging Advantage’s cloud based professional radiology physician and technology capabilities.
The agreement creates scale and breadth of capabilities, combining the strengths of both outpatient imaging and radiology professional services. The parties’ expansion plans in 2016 may include additional markets.
