This paper provides a comparative analysis of various deep learning models, including recurrent neural networks (RNNs), Generative Adversarial Networks (GANs), and hybrid approaches, focusing on their accuracy in healthcare applications. Our comparative study assesses these models across different healthcare tasks, such as image classification, disease prediction, and patient risk stratification. The findings indicate that hybrid approach in medical image analysis with high accuracy rates, RNNs are particularly effective in handling sequential data from patient records. GANs significantly contribute by generating realistic synthetic data to augment training datasets, thereby enhancing model robustness and performance. Hybrid approaches, which combine elements of these models, demonstrate superior accuracy and versatility in handling complex, multimodal healthcare data.