As deep learning models and datasets expand, the demand for computational resources and memory storage intensifies; at the same time, data privacy concerns hinder data and model sharing. Hence, accessibility of model training is significantly challenged. Vector embeddings, as compact representations of medical images, offer a solution to the challenges of computational resource demands and data privacy concerns in AI-based medical imaging. In this study we investigate the suitability of vector embeddings as substitutes for original medical images in disease prediction tasks, focusing on performance and fairness. Using datasets like MIMIC-CXR and CheXpert, we find that vector embedding-based models generally improve disease detection performance and mitigate unfairness in diagnosis rates. The reduced demographic signals in these embeddings may contribute to fairer outcomes without compromising performance. Our findings suggest that vector embeddings can enable more accessible and equitable medical computer vision, particularly in low-resource settings.