Connected Technology
We are working towards a more connected health care system by leveraging patient-generated health data and building digital applications utilizing remote sensors and wearable technologies.
Research
Leading the digital health revolution
Our research leverages the latest technologies to create novel methodologies and bring together interdisciplinary expertise.
Cutting Edge Research
Health IT Systems Engineering
We are building state-of-the-art computational frameworks and architectures to enable rapid scientific discovery alongside translational applications into real-world clinical settings.
Multimodal Data
By creating an innovative environment to access electronic health records, imaging, -omics, and sensor data, we will advance healthcare-driven Artificial Intelligence (AI)-based solutions.
Machine Learning and Artificial Intelligence
We are advancing data science methodologies in medicine with insights that benefit patients and healthcare providers through better clinical prediction and early warning systems.
Core Research Projects
Leveraging expertise in computer science and engineering, we've built a platform where researchers can access the vast datasets held at Mount Sinai for data science and machine learning endeavors.
View full projectEhive, created by the Digital Discovery Program, is comprehensive digital health research platform of patient-centric health studies using wearable, mobile and sensor technologies to better understand complex diseases.
View full projectThe Warrior Watch platform will help predict asymptomatic or pre-symptomatic COVID-19 infections as well as how the pandemic is impacting the stress and resilience of our workforce, here at Mount Sinai
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The HPI･MS research exchange is a co-mentorship program supported by research faculty at the Hasso Plattner Institute and the Icahn School of Medicine at Mount Sinai, wherein trainees lead innovative projects that leverage the unique clinical data resources of Mount Sinai with the applied digital engineering training of HPI in order to improve clinical computational understanding.
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