Leading the Way With Artificial Intelligence

For years, the Icahn School of Medicine at Mount Sinai and the Mount Sinai Health System have been pioneering the integration of artificial intelligence (AI) into health care. Our initiatives are creating powerful AI tools for both clinical and research purposes. Our goal: to make health care safer, smarter, and more efficient.

Landmark Achievements in AI Over the Years

Mount Sinai clinicians and investigators have been early adopters of artificial intelligence, and have paved the way for advances in precision medicine, population health, and early interventions. From harnessing the power of genomics to preventing falls, researchers and clinicians at Mount Sinai are using AI to impact lives for the better.

Predicting Patients at Risk

Researchers used an advanced AI algorithm to improve prediction of diseases by analyzing de-identified data from patients across the Mount Sinai Health System. In the study published in Scientific Reports, our faculty found that their algorithm significantly outperformed evaluations based on raw data from electronic health records and had impressive results in predicting severe diabetes, schizophrenia, and various cancers.

Foreseeing the Progression of Diseases

Mount Sinai pathologists use artificial intelligence to characterize tissue samples in patients with certain diseases, including prostate and breast cancer, to more accurately predict the course of the disease, as well as to recognize and quantify accumulation of abnormal proteins such as those in Alzheimer’s disease. Artificial intelligence provides a precise mathematical approach to classifying and treating disease, which improves diagnoses, refines treatment plans, and improves outcomes.

Improving the Safety of Inpatients

Mount Sinai’s AI-powered system helps clinicians identify and prioritize patients at risk for conditions such as cardiopulmonary deterioration, malnutrition, and falls. Developed by the Clinical Data Science team under the direction of David Reich, MD, President and Chief Operating Officer of The Mount Sinai Hospital, the system supports clinical decision-making for thousands of patients each day. As the algorithms continue to “learn” from real-time data, their accuracy and performance improves.

Population Health and a New Model of Health Care

In the Department of Population Health, Niyum Gandhi, Executive Vice President and Chief Population Health Officer of the Mount Sinai Health System, has been using machine-learning algorithms to mine data that identifies who among the system’s 500,000 patients is at risk for unplanned admissions to Mount Sinai’s hospitals. Predictive modeling is enabling Mount Sinai to transition to a delivery model focused on value and risk-based population health.

Targeting At-Risk Patients Through Precision Medicine

More than 47,000 DNA and blood serum specimens from Mount Sinai’s diverse population of patients are housed at the BioMe Biobank, which is part of the Charles Bronfman Institute for Personalized Medicine. The Mount Sinai team is using these specimens in conjunction with AI to determine which patients may be at risk for kidney failure and the need for dialysis. BioMe is also linked to Mount Sinai’s electronic health records, and AI enables scientists to conduct genetic, epidemiologic, molecular, and genomic studies rapidly and efficiently with large collections of research specimens linked with medical information.

 “Artificial Intelligence and machine learning are spurring innovation across many different fields, but perhaps most significantly in health care. Mount Sinai has proven itself a pioneer in data mining to improve patient diagnosis and treatment, and I am pleased to support its mission and accelerate the development of cutting-edge therapies and technologies that have the potential to change lives around the world.”
 —Hamilton Evans “Tony” James