In-Context Learning for Data-Efficient Diabetic Retinopathy Detection via Multimodal Foundation Models.

ABSTRACT:

Objective

This study aims to evaluate whether in-context learning (ICL), a prompt-based learning mechanism enabling multimodal foundation models to rapidly adapt to new tasks without retraining or large annotated datasets, can achieve comparable diagnostic performance to domain-specific foundation models. Specifically, we use diabetic retinopathy (DR) detection as an exemplar task to probe if a multimodal foundation model (Google Gemini 1.5 Pro), employing ICL, can match the performance of a domain-specific model (RETFound) fine-tuned explicitly for DR detection from color fundus photographs (CFPs).

Design

A cross-sectional study.

Subjects

A retrospective, publicly available dataset (Indian Diabetic Retinopathy Image Dataset) comprising 516 CFPs collected at an eye cl

SUBMITTER: Ayhan MS 

PROVIDER: S-EPMC12548082 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature