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Affiliated institutions

  • Feinstein Institutes (opens in new tab)
  • Northwell Health (opens in new tab)
  • Zucker School of Medicine (opens in new tab)

© 2026 Division of Health AI, Northwell Health. All rights reserved.

Team

Theofilos Kanavos, MD

Elmezzi Scholar

Theofilos Kanavos, MD, is an Elmezzi Scholar and physician researcher at the Division of Health AI at the Feinstein Institutes for Medical Research, Northwell Health. His research applies machine learning and deep learning to medical imaging and clinical data across oncology, ophthalmology, and sleep medicine. With the Zanos lab, he has co-authored systematic reviews on deep learning for interpreting lymphoma PET images and on AI-driven differentiation of uveal melanoma from nevus on fundus photographs, plus a machine learning study screening women for moderate-to-severe obstructive sleep apnea. He currently leads work on an LLM-powered framework for delirium annotation and early prediction from clinical and physiological data.

4 projects·3 papers

Theofilos Kanavos
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Research

Projects

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.

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.

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.

ONSET

Federally funded · R01AG100193

Delirium identification and prediction

Machine learning that identifies delirium in hospitalized older adults from clinical notes, using large language models, and predicts who is at risk before it develops.

Publications

Selected papers

RespirationApr 2026

Bridging the Gender Gap in Obstructive Sleep Apnea: A Machine Learning Approach to Screening Women for Moderate-to-Severe Disease

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Translational Vision Science & TechnologyJan 2026

Artificial Intelligence-Driven Differentiation Between Uveal Melanoma and Nevus Based on Fundus Photographs: A Systematic Review and Meta-Analysis

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CancersDec 2024

A Systematic Review of the Applications of Deep Learning for the Interpretation of Positron Emission Tomography Images of Patients with Lymphoma

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More of the division

Full roster
SK

Sarah Khan

Visiting Scholar

Elly Konjkav, MS

Health Data Scientist

DK

Demi Kostaridis

Visiting Scholar