2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs
David Restrepo, Chenwei Wu, Luis Filipe Nakayama +4
Progress in AI-based retinal image analysis has accelerated with the adoption of foundation models, yet evaluating their real-world reliability remains challenging. Performance rep…
Medical Context Distorts Decisions in Clinical Vision Language Models
David Restrepo, Ira Ktena, Maria Vakalopoulou +2
Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require integrating both visual and textua…
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
David Restrepo, Miguel L Martins, Chenwei Wu +5
Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contri…
Implicit Bias in LLMs for Transgender Populations
Micaela Hirsch, Marina Elichiry, Blas Radi +6
Large language models (LLMs) have been shown to exhibit biases against LGBTQ+ populations. While safety training may lessen explicit expressions of bias, previous work has shown th…