GANs in Medical Diagnostics and Classification explores the role of Generative Adversarial Networks (GANs) in addressing underdiagnosed and overlooked health challenges. The book bridges cutting-edge AI research with real-world medical applications, presenting interdisciplinary insights into how GANs enhance diagnostics, treatment precision, and disease surveillance across domains such as antimicrobial resistance, rare genetic disorders, and environmental health risks. It also examines ethical, policy, and accessibility dimensions of AI in healthcare. By combining technical depth with practical case studies, this volume equips medical researchers, AI engineers, and healthcare professionals with actionable knowledge to tackle silent epidemics using GAN-based tools.

• Delivers real-world case studies that demonstrate GAN applications in diagnosing rare diseases, detecting antimicrobial resistance, and enhancing medical imaging
• Integrates interdisciplinary perspectives from medicine, AI, ethics, and policy to provide a holistic view of AI-driven healthcare innovation
• Explores emerging technologies such as synthetic data generation, wearable diagnostics, and AI-enhanced telemedicine for future-ready healthcare solutions