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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

Effrosyni Birbas, MD

Elmezzi Scholar

Effrosyni Birbas, MD, earned her medical degree from the Faculty of Medicine at the University of Ioannina, Greece, in 2024. Following her graduation, she joined the Division of Health AI, where she has been involved in multiple projects exploring applications of AI in medicine, including a machine learning approach for obstructive sleep apnea screening in women and the development of LLM-based frameworks for automated annotation of clinical charts for various clinical tasks, such as delirium and medication-related readmission detection. As a PhD student at the Elmezzi Graduate School of Molecular Medicine at Northwell, her current research focuses on endometriosis, a disease characterized by marked diagnostic delays and high clinical heterogeneity. Specifically, she aims to integrate multimodal electronic health record (EHR) data with AI to enable earlier disease detection and identify clinically meaningful phenotypes.

4 projects·3 papers

Effrosyni Birbas
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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
LC

Lucas Cang

Visiting Scholar

Shubham Debnath, PhD

Senior Research Scientist

TE

Thea Enache

Visiting Scholar