Generative AI in Healthcare: A New Frontier for Diagnostics

This chapter investigates the transformative potential of Generative AI in healthcare diagnostics, focusing on medical imaging. The core hypothesis posits that GANs and VAEs can significantly enhance diagnostic accuracy by augmenting limited datasets and improving image quality. We apply these models on the ChestX-ray14 dataset. GANs are utilized to generate synthetic chest X-rays for data augmentation, while VAEs assist in anomaly detection by reconstructing scans to highlight abnormalities. A CNN-based classifier is trained on both real and GAN-augmented images. Performance metrics, including AUC, precision, and recall, are evaluated before and after augmentation. Results show a 5–7% improvement in diagnostic performance, validating the models&s; efficacy. Additionally, we discuss the ethical and clinical challenges, offering directions for future research in federated learning and hybrid AI systems.