Updated on Nov 19, 2025 | Announcements, Artificial Intelligence
We are pleased to announce the establishment of a new Division of Data Driven and Digital Medicine within the Department of Medicine at the Icahn School of Medicine at Mount Sinai. Led by Girish N. Nadkarni, MD, the Division’s mission is to create a data science and digital health hub to catalyze translational research.
Recognizing that so much of what we do is driven by data, we are establishing this division as a critical step toward integrating data science and digital tools into clinical practice. Creating a hub within the Department of Medicine that promotes data science and digital health resources will augment and empower translational research and clinical care. Another priority for this new Division will be exposing medical students, residents, and fellows to the world of data science and digital health. The division will collaborate with many Icahn Mount Sinai entities, including the Mount Sinai Clinical Intelligence Center, the Hasso Plattner Institute for Digital Health at Mount Sinai, the Clinical Data Science team, The Charles Bronfman Institute for Personalized Medicine, the Scientific Computing and Data Science team, and others.
Dr. Nadkarni has demonstrated his passion for and skill with data-driven science, most recently as co-director of the Mount Sinai COVID Informatics Center. Along with Alexander Charney, MD, and a transdisciplinary group of collaborators, Dr. Nadkarni led data-driven efforts to understand and address COVID-19, which led to clinical trials that have had an impact on clinical care.
Dr. Nadkarni’s scientific interests beyond COVID-19 are reflected in his role as Principal Investigator on several projects funded by the National Institutes of Health and his authorship of more than 200 peer-reviewed publications. Additionally, he is a named inventor on multiple patents. In 2018, Dr. Nadkarni was a scientific cofounder of RenalytixAI, an artificial intelligence-enabled in vitro diagnostics company that collaborates with Mount Sinai in seeking to improve chronic kidney disease detection, management, and treatment.
By creating the Division of Data Driven and Digital Medicine, the Department of Medicine now has one of the first data science and digital health divisions of Medicine in the country, putting us at the forefront of an emerging field. We are extremely proud that we are breaking new ground by its establishment.
Please congratulate Dr. Nadkarni on his leadership role in this initiative.
Dennis S. Charney, MD, Anne and Joel Ehrenkranz Dean, Icahn School of Medicine at Mount Sinai, President for Academic Affairs, Mount Sinai Health System
David C. Thomas, MD, MHPE, Acting Chair, Department of Medicine, Vice Chair of Education, Department of Medicine, Mount Sinai Health System
Nov 25, 2019 | Artificial Intelligence, Featured, Research
Keynote speaker Michael Snyder, PhD
What is the role of artificial intelligence (AI) in medicine, and how is it changing the practice of health care as we know it? That was the subject of the eighth annual SinaInnovations Conference, held Tuesday, October 15, and Wednesday, October 16, in Stern Auditorium. The event featured leading physicians and scientists from academia and industry who spoke about their work in deploying AI—the most powerful technology under development—to augment discovery and clinical use.
Experts shared their experiences in using AI in a variety of ways, from medical imaging, to predicting disease, to keeping people healthy, and highlighted the massive transformation taking place in health care and medicine, where software is driving innovation.
Michael Snyder, PhD, Chair of the Department of Genetics, and Director of the Center for Genomics and Personalized Medicine at Stanford University, a keynote speaker, discussed the role of AI in advancing personalized medicine. “I see a world where, with genome sequencing and continuous monitoring using wearable devices, we can better manage people’s health and hopefully do this at an individual level, and have personal machine-learning algorithms that follow people and their health state,” Dr. Snyder said. “We’re very capable of measuring more things, and here’s an area where AI can make a big impact.”
Melissa A. Haendel, PhD, Director of Translational Data Science at Oregon State University, spoke about her work in leading the federally funded Monarch Initiative, which is building sophisticated algorithms that integrate a multitude of data about rare diseases in order to improve research and clinical care. “No one group is actually annotating a disease model that has all the same attributes,” Dr. Haendel said. “We can’t even count the number of rare diseases.” Her team’s goal, she said, is to pull all of the data together and use it to build models that help physicians make earlier diagnoses, identify biomarkers of disease, and find better treatments.
Conference participants Heather J. Lynch, PhD, Associate Professor of Ecology and Evolution, Stony Brook University, left, and Georgia D. Tourassi, PhD, Director, Health Data Sciences Institute, Oak Ridge National Laboratory.
David Sontag, PhD, Associate Professor of Electrical Engineering and Computer Science at Massachusetts Institute of Technology, discussed how AI can be used to redesign electronic medical records so they can yield more reliable information on the patient’s risk for various diseases. In one case, he said, his team developed a machine-learning algorithm to help an infectious disease clinician at Massachusetts General Hospital and Brigham and Women’s Hospital reduce the number of unnecessary prescriptions for antibiotics.
AI is already playing a role in augmenting radiology. Keith J. Dreyer, DO, PhD, Vice Chairman, Radiology, at Massachusetts General Hospital and Chief Science Officer of the American College of Radiology, told the audience that “AI has huge value” and will be increasingly useful over time as the field matures.
In his keynote address, Pieter Abbeel, PhD, an entrepreneur and Professor of Electrical Engineering and Computer Science at the University of California, Berkeley, showed how deep machine learning takes place through constant repetition. In one example, he illustrated how a robot learns to run. After 2,000 iterations, it will become proficient. By comparison, a healthy human child would learn to run proficiently after roughly two weeks of practice. In many cases, he said, machines have achieved human-level error rates.
Among the many algorithms Stanford University is working on is one that recognizes the photo of a radiological image taken with a mobile phone, according to Curtis Langlotz, MD, PhD, Director of Stanford’s Center for Artificial Intelligence in Medicine and Imaging. This technology would allow general practitioners and other health care professionals in remote areas to use their mobile phones to access an algorithm that would assist them in making medical decisions when a radiologist is not available. For example, they would be able to determine whether a patient with, say, tuberculosis, should be discharged from the hospital.
Speakers and attendees at the conference agreed that AI is both promising and challenging. Suresh Venkatasubramanian, PhD, Professor of Computing at the University of Utah, cautioned that inherent bias in the data will create bias in the algorithms. “Models are fragile,” he said. “The Achilles heel is that the more sophisticated a system gets, especially with deep learning, the more sensitive it gets to small perturbations, and this could wreak havoc on the system.”
Greg Zaharchuk, MD, PhD, Professor of Radiology (Neuroimaging and Neurointervention) at Stanford University, concluded his talk with a nod to the future. “I think we’re only scratching the surface. This is a moment of extreme creativity, and it’s a very exciting time to be in the field.” Rather than replacing radiologists and other medical specialists, he added, AI “is really going to extend our abilities as physicians.”
New Gift Supports Young Entrepreneurs at Mount Sinai
This year, for the first time, a nonprofit biotech accelerator company founded by five former postdocs at the Icahn School of Medicine at Mount Sinai presented the school with a five-year, $50,000 gift to support young entrepreneurs in the New York City area whose science is being used to create therapies, devices, and diagnostics that support human health. The gift from The Keystone for Incubating Innovation in Life Sciences (KiiLN) went to Raymond A. Alvarez, PhD, Assistant Professor of Medicine (Infectious Diseases) at the Icahn School of Medicine at Mount Sinai, who is working on a platform that identifies and studies the antibodies of individuals who are immune to hantaviruses, which are spread by rodents and have a 38 percent mortality rate. Currently, there are no vaccines or treatments for hantaviruses.
Nov 25, 2019 | Artificial Intelligence, Featured, Research
The three team finalists were: George: Katie Depue, David Koellhofer, and Brendan Reilly. Deliberate: Marc Aafjes, Michael Balangue, Do Hyung Kwon, Hansaim Lim, and Paulo Serodio. Deep Brain Precision: John Di Capua, Taylor Miller, Ashley So, and Danielle Soldin.
One hundred eighty medical and graduate students, and others, formed 19 teams to participate in the fourth annual Mount Sinai Health Hackathon in October. The 48-hour competition, held over the weekend leading up to the SinaInnovations Conference, challenged the participants to create novel health care solutions that would expand the limits of human performance.
Three teams each received checks totaling $2,500 and will have the opportunity to pitch their ideas again in 2020 at Mount Sinai’s Innovation Showcase before a group of entrepreneurs and venture capitalists. They will be joined by a fourth wild-card team chosen from the non-finalists.
Scott L. Friedman, MD, Dean for Therapeutic Discovery, and Chief of the Division of Liver Diseases at the Icahn School of Medicine at Mount Sinai, told the participants, “We started this event four years ago as part of a larger effort to spur innovation. This is really the embodiment of our values about teamwork and doing great things.”
The three team finalists were:
Deliberate: Improving the quality of care in psychotherapy through confidential recording and analysis. Team members: Marc Aafjes, Michael Balangue, Do Hyung Kwon, Hansaim Lim, and Paulo Serodio.
George: An artificial intelligence application that allows dialysis providers to optimize scheduling and improve a clinic’s efficiency. Team members: Katie Depue, David Koellhofer, and Brendan Reilly.
Deep Brain Precision: An app that would allow physicians to monitor a patient’s progress after receiving deep brain stimulation for Parkinson’s disease and other motor disorders. Team members: John Di Capua, Taylor Miller, Ashley So, and Danielle Soldin.
Sponsors and partners: Accenture; Altice Business; Cisco; Farmer’s Fridge; Kitware; PepsiCo; Persistent Systems; the National Center for Advancing Translational Sciences, National Institutes of Health; and the Christopher & Dana Reeve Foundation.
