Abstract Recent advancements in artificial intelligence (AI) have significantly enhanced the efficiency and quality of nuclear cardiology imaging. AI algorithms, such as generative adversarial networks (GAN) and deep learning (DL) models, are improving image acquisition by reducing noise and correcting motion to minimize radiation exposure while maintaining diagnostic accuracy. AI has also demonstrated the potential to create synthetic attenuation correction (AC) imaging, accounting for soft-tissue attenuation without added computed tomography (CT) imaging. In patients with CT attenuation imaging, AI can […]