Theodoros Zanos, PhD | Division of Health AI

Professor & AVP

Dr. Theodoros (Theo) Zanos, PhD is a Professor & AVP, and the head of the Division of Health AI at Northwell Health and the Neural and Data Science Lab at the Institute of Health System Science and Institute of Bioelectronic Medicine, at the Feinstein Institutes for Medical Research and the Zucker School of Medicine, Hofstra Northwell. He received his Engineering diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in Greece, his MSc and PhD in Biomedical Engineering from the University of Southern California and postdoctoral training at the Montreal Neurological Institute at McGill. His current research focuses on developing and applying AI/machine learning methods on multimodal healthcare, neural and physiological data to enable early diagnosis, disease severity assessment, and personalization and adaptability of therapies. He has been awarded multiple federal and industry grants, totaling >$15M of external funding from NIH, CDC and other federal and industry sources, and published >70 peer-reviewed papers, in journals such as Nature Communications, Nature Machine Intelligence, PNAS, JAMA, npj Digital Medicine, Neuron (Cell Press) and others. He has been awarded the Northwell Excellence in Research Award three times, inducted to the National Academy of Inventors as a senior member, received the Modern Healthcare 2026 Innovator award, finalist in Fast Company’s World Changing Ideas in AI, twice finalist in Northwell’s Innovation Challenge, and received the Jean Timmins Award and the Center of Excellence in Commercialization and Research Award.

17 projects59 papers

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Research

Projects

Federally funded

Health AI Assurance

FDA-funded validation of electrocardiogram devices and independent evaluation of clinical AI on Northwell's own patients, before and after a tool reaches the bedside.

Federally funded · R01NR020774

In-hospital deterioration prediction

Deep learning models that estimate a hospitalized patient's risk of deterioration continuously, from the electronic health record and from wearable sensors, so that clinicians are warned hours before a rapid response or ICU transfer.

Elmezzi Scholars

Sleep apnea screening in women

A machine learning approach to screening women for moderate-to-severe obstructive sleep apnea, a condition underdiagnosed in women because standard screening was built around male presentation.

Federally funded · U01OH012050

Transcutaneous auricular vagus nerve stimulation for PTSD in World Trade Center responders

A feasibility and acceptability study of daily, non-invasive auricular vagus nerve stimulation to reduce PTSD symptoms among 9/11 responders, twenty years after the attacks.

Elmezzi Scholars

Uveal melanoma screening from fundus photographs

A systematic review and meta-analysis of artificial intelligence that distinguishes uveal melanoma from benign nevus on fundus photographs, the basis for automated screening in ophthalmology.

Division project

Computational models of vagus nerve stimulation

Histologically realistic models of the cervical vagus nerve that predict which fibers a stimulation pattern will activate, used to design selective, organ-specific vagus nerve stimulation.

Division project

Decoding the vagus nerve: preclinical recording and interpretation

Recording from the mouse vagus nerve, chronically and wirelessly, and decoding what its neurons report about inflammation and metabolism, toward bioelectronic devices that diagnose and treat from neural signals.

Division project

Nursing workforce forecasting

Machine learning models using DeepAR probabilistic forecasting predict nursing workforce demand across Northwell's hospital units up to 12 months ahead, supporting preemptive hiring and staffing decisions across diverse specialties.

Elmezzi Scholars

Predicting response to vagus nerve stimulation in drug-resistant epilepsy

Machine learning models can predict which patients with drug-resistant epilepsy will respond to vagus nerve stimulation therapy, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.84 across 12 studies comprising 535 patients.

Elmezzi Scholars

Deep learning in pancreatic cancer and PET interpretation

Machine learning to predict completion of treatment for pancreatic cancer, and systematic reviews of deep learning for PET image interpretation and for pancreatic cancer care.

Division project

Maternal fever / neonatal sepsis prediction

Continuously monitored vital signs and heart rate variability during labor predict maternal fever 2-3 hours before clinical onset, with area under the curve of 0.748, enabling early detection of mothers at risk for neonatal early-onset sepsis.

Federally funded · SPARC

REVA: the human vagus nerve atlas

A comprehensive anatomical dataset of 60 human vagus nerves (30 left, 30 right), spanning millions of micro-CT images across hundreds of terabytes of data. Using 3D nnU-Net segmentation, this project builds a detailed vagus digital twin to guide the design of selective vagus nerve stimulation therapies, part of a $6.7 million NIH SPARC award in collaboration with the TNP Lab.

Archive · 5

Publications

Publications across the division

Author or co-author on 59 peer-reviewed publications across the division.

All 59 publications

More of the division

Full roster

Georgia Agoritsas

Visiting Scholar

Soroush Arabshahi, PhD

Senior Biomedical Engineer

Effrosyni Birbas, MD

Elmezzi Scholar