Ophthalmic AI With Just Ten Examples: Comparing Multimodal Large Language Models, Vision Foundation Models, and Vision-Language Models

Purpose: To compare multimodal large language models (MLLMs), vision foundation models (VFMs), and contrastive vision-language models (VLMs) for ophthalmic image interpretation with no labeled examples (zero-shot) or as few as 10 labeled examples per class (10-shot). Design: Retrospective benchmarking study. Subjects: 4 public datasets (UCSD, IDRiD, REFUGE, ASOCT-MSDC) and 3 private datasets (OHTS, BPEI-DLS, WashU-OCT) of anterior- and posterior-segment ophthalmic imaging. Methods: Nine AI models were evaluated across 3 architectural families: MLLMs (Qwen3.8-27B, GLM-5.3-Flash, Gemma-4-31B-it)

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