Typical machine learning models typically demand a substantial volume of data for effective training, ensuring optimal performance during testing. However, these models often fail to specify the extent of data required—how big data is big enough to begin with? The “label few, classify many” paradigm is interesting in such a scenario where data is limited, and this holds true for healthcare informatics like others. To address this challenge, we explore the application of n-shot GANs in generating synthetic images, particularly in the context of chest X-rays for detecting COVID-19. Starting with one-shot GAN, our findings reveal that training on just 160 healthy chest X-rays achieves outstanding results, with maximum AUCs of 97.2 and 97.4 from ROC and PR curves on a test dataset comprising approximately 3600 chest X-rays. These results demonstrate comparable performance with state-of-the-art approaches in the field.