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In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.
Current public health measures catalyzed a large shift to virtual care, resulting in a great uptake in telephone and video-enabled care. While pre-pandemic public healthcare funding rarely covered the telephone as a reimbursable care delivery model, it has proven a crucial offering for many populations. As the new standard of virtual service delivery is being solidified, simple technological solutions that provide access to care must continue to be supported. This paper explores an important consequence of relying on complex technologies as the new standard of virtual care: the risk of exacerbating health disparities by enabling a deeper digital divide for marginalized populations.