Explore model-based deep learning applications in imaging and communications through this comprehensive conference talk. Delve into the integration of parametric models with optimization tools, leading to efficient and interpretable networks. Discover approaches to overcome challenges in deep neural networks, such as their black-box nature and large training set requirements. Examine case studies in image deblurring, separation, and super-resolution for ultrasound and microscopy. Learn about efficient communication systems and the application of model-based methods for COVID-19 diagnosis using X-ray and ultrasound. Gain insights into topics including deep symbol detection, data-driven hybrid algorithms, deep unrolling for blind deblurring, and sparsity-based ultrasound super-resolution hemodynamic imaging. Understand the balance between classical statistical modeling techniques and deep learning approaches in signal processing and communications.