  Large-scale multi-modal deep learning models have revolutionized domains such
as healthcare, highlighting the importance of computational power. However, in
resource-constrained regions like Low and Middle-Income Countries (LMICs),
limited access to GPUs and data poses significant challenges, often leaving
CPUs as the sole resource. To address this, we advocate for leveraging vector
embeddings to enable flexible and efficient computational methodologies,
democratizing multimodal deep learning across diverse contexts.
  Our paper investigates the efficiency and effectiveness of using vector
embeddings from single-modal foundation models and multi-modal Vision-Language
Models (VLMs) for multimodal deep learning in low-resource environments,
particularly in healthcare. Additionally, we propose a simple yet effective
inference-time method to enhance performance by aligning image-text embeddings.
Comparing these approaches with traditional methods, we assess their impact on
computational efficiency and model performance using metrics like accuracy,
F1-score, inference time, training time, and memory usage across three medical
modalities: BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench
(public health).
  Our findings show that embeddings reduce computational demands without
compromising model performance. Furthermore, our alignment method improves
performance in medical tasks. This research promotes sustainable AI practices
by optimizing resources in constrained environments, highlighting the potential
of embedding-based approaches for efficient multimodal learning. Vector
embeddings democratize multimodal deep learning in LMICs, particularly in
healthcare, enhancing AI adaptability in varied use cases.
