Embedding vs Image-Based AI: A Comparative Fairness Study in Chest X-ray Analysis

Abstract

AI has shown remarkable potential in healthcare, but faces accessibility challenges due to high computational and expertise demands, especially in medical image analysis. Vector embeddings, compact representations of medical images achieved from foundation models in zero-shot inference, offer a potential solution. Recently, an equivalent vector embeddings dataset of existing large publicly available medical images has been released, for which training an AI model requires significantly lower computing infrastructure and storage needs. Such data sets provide greater accessibility to AI in medical imaging for those who do not have access to large computing resources. The burning question remains: What is the gain or loss in using vector embedding to replace medical images, particularly from a fairness and utility point of view? In this work, we compare AI models trained in vector embeddings (Emb) with raw chest radiograph images for disease diagnosis, focusing on both performance and fairness. Our results show that Emb-based models match or exceed image-based models in diagnostic performance while improving fairness. Crucially, Emb achieves this with far less computational cost. These findings position Emb as a powerful, scalable alternative to image-based AI, especially valuable for low-resource settings where access to GPUs and expert infrastructure is limited.