Multimodal LLMs for retinal disease diagnosis via OCT: few-shot versus single-shot learning.

Journal: Therapeutic advances in ophthalmology
Published Date: May 20, 2025

Abstract

BACKGROUND AND AIM: Multimodal large language models (LLMs) have shown potential in processing both text and image data for clinical applications. This study evaluated their diagnostic performance in identifying retinal diseases from optical coherence tomography (OCT) images.

Authors

  • Reem Agbareia
    Ophthalmology Department, Hadassah Medical Center, Jerusalem, Israel.
  • Mahmud Omar
    Tel-aviv university, Faculty of medicine, Tel-Aviv, Israel. Electronic address: Mahmudomar70@gmail.com.
  • Ofira Zloto
    Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel. ozloto@gmail.com.
  • Benjamin S Glicksberg
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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