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Toward Multimodal Conversational AI for Age-Related Macular Degeneration

Ran Gu¹, Benjamin Hou¹, Mélanie Hébert²

¹Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

Ophthalmology. Retina

|August 18, 2026

Abstract

Purpose:

To evaluate OcularChat, an age-related macular degeneration (AMD)-specific multimodal large language model for interpreting color fundus photographs.

Methods:

A general-purpose multimodal large language model was fine-tuned using 705,850 simulated patient-physician dialogues paired with 46,167 AREDS images, then tested on separate AREDS and AREDS2 datasets.

Results:

In AREDS, OcularChat correctly classified advanced AMD, pigmentary abnormalities, and drusen size in 95.4%, 84.9%, and 67.8% of images, respectively. Retina specialists rated its responses more highly than those of the same model without fine-tuning.

Conclusions:

OcularChat gives the potential to support clinician-supervised, interpretable AMD image review, research annotation, and education, requiring prospective validation before clinical deployment.

Keywords:

age-related macular degeneration color fundus photographs multimodal large language models visual question answering

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