Future Directions and Emerging Trends in GANs and Meta-Learning Impact on Medical Diagnostics
Kande Archana, V. Kamakshi Prasad · Advances in computational intelligence and robotics book series · 2025
Generative Adversarial Networks (GANs) and meta-learning are driving significant advancements in medical diagnostics. GANs are progressing through algorithms like StyleGAN, BigGAN, and CycleGAN, enhancing image synthesis, realism, and domain translation. These improvements are crucial for medical imaging, enabling the creation of high-quality, interpretable diagnostic images. Conditional GANs (cGANs) facilitate targeted image generation, supporting the development of specialized diagnostic tools, while self-supervised GANs reduce dependency on extensive labeled datasets, making advanced medical AI more scalable. Meta-learning, or “learning to learn,” enhances AI adaptability and efficiency. Algorithms such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks allow models to generalize from limited data, common in medical diagnostics. These techniques enable rapid adaptation to new diagnostic tasks, supporting personalized and precise medical interventions.