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The success of deep learning is largely due to the availability of largeamounts of training data that cover a wide range of examples of a particularconcept or meaning. In the field of medicine, having a diverse set of trainingdata on a particular disease can lead to the development of a model that isable to accurately predict the disease. However, despite the potentialbenefits, there have not been significant advances in image-based diagnosis dueto a lack of high-quality annotated data. This article highlights theimportance of using a data-centric approach to improve the quality of datarepresentations, particularly in cases where the available data is limited. Toaddress this "small-data" issue, we discuss four methods for generating andaggregating training data: data augmentation, transfer learning, federatedlearning, and GANs (generative adversarial networks). We also propose the useof knowledge-guided GANs to incorporate domain knowledge in the training datageneration process. With the recent progress in large pre-trained languagemodels, we believe it is possible to acquire high-quality knowledge that can beused to improve the effectiveness of knowledge-guided generative methods.

Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions