Health AI Central
Peer-Reviewed Publication
NPJ Digit Med2025;8(1):576.September 25, 2025Journal Article

Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.

José Morano1,2, Botond Fazekas3,4, Emese Sükei5, Ronald Fecso3,4, Taha Emre3,4, Markus Gumpinger5, Georg Faustmann3,4, Marzieh Oghbaie3,4, Ursula Schmidt-Erfurth5, Hrvoje Bogunović6,7
1Christian Doppler Laboratory for Artificial Intelligence in Retina, Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria. jose.moranosanchez@meduniwien.ac.at.
2Comprehensive Center for AI in Medicine, Medical University of Vienna, Vienna, Austria. jose.moranosanchez@meduniwien.ac.at.
3Christian Doppler Laboratory for Artificial Intelligence in Retina, Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
4Comprehensive Center for AI in Medicine, Medical University of Vienna, Vienna, Austria.
5OPTIMA Lab, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria.
6Christian Doppler Laboratory for Artificial Intelligence in Retina, Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria. hrvoje.bogunovic@meduniwien.ac.at.
7Comprehensive Center for AI in Medicine, Medical University of Vienna, Vienna, Austria. hrvoje.bogunovic@meduniwien.ac.at.
PubMed PMC DOI PDF

Abstract

Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to underperform on independent, unseen data. Foundation models (FMs), large AI models trained on vast, unlabeled datasets, have shown promise in ove…

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