<p>Deep learning, a subset of machine learning based on multi-layered artificial neural networks, has emerged as a powerful paradigm for pattern recognition and predictive analytics from large-scale data. This paper presents a comprehensive review of foundational deep learning architectures, including autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), along with their variants. The evolution of deep learning from early perceptrons to modern pre-training strategies is outlined. Particular emphasis is placed on healthcare applications, where deep learning has demonstrated remarkable performance in medical imaging, physiological signal analysis, disease diagnosis, and pandemic response (especially COVID-19 detection and classification). Advantages, limitations, and comparative performance of major algorithms are discussed. Finally, current challenges and future research directions in healthcare-oriented deep learning are highlighted.</p>

<p><strong>Index Terms: </strong>Deep learning, artificial neural networks, autoencoders, convolutional neural networks, recurrent neural networks, healthcare informatics, medical imaging, COVID-19 diagnosis.</p>