5 citations · 11 across the 6 of their papers we have counts for
7 papers
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…
A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings
Hei Ting, Hei Ting Chan, Chan +9
Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarc…
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications
David Restrepo, Chenwei Wu, Sebastián Andrés Cajas +3
Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained re…
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
David Restrepo, Chenwei Wu, Constanza Vásquez-Venegas +3
In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for…
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
David Restrepo, Chenwei Wu, Constanza Vásquez-Venegas +5
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient o…
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Yevgeniy Men, Jonathan Fhima, Leo Anthony Celi +3
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss. Timely identification is critical to curb vision impairment. Al…
Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial Intelligence Lifecycle: A Review
Luis Filipe Nakayama, João Matos, Justin Quion +8
Over the past two decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, an…