Deep learning is a subdiscipline of machine learning that has gained a lot of attention in the recent past due to its effectiveness in applying artificial neural networks for pattern recognition and information extraction from large datasets, thus enabling automation of decision-making processes. Backpropagation, autoencoders, restricted Boltzmann machines, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsule networks, Transformers, and other significant deep learning algorithms and their architectures will all be covered in this chapter. The effectiveness of these algorithms has been shown in many research domains, but the healthcare sector has experienced some of the most success. This chapter examines the relationship between deep learning and medical virology and applies deep learning to precision clinical decision-making for viral diseases like human immunodeficiency virus (HIV) and HPV. The development of targeted therapies, the analysis of viral genomic data to look for strain differences, and diagnostic imaging of viral-induced abnormalities are some of the main applications discussed. The role of deep learning techniques in enhancing the detection and management of cervical abnormalities associated with HPV and HIV-related lung diseases is also explored. These technologies also raise significant practical challenges, e.g., in terms of data confidentiality, model interpretability, and neural architecture search. Possible outcomes of these issues are presented in the recent works on AutoML-Zero, federated learning, and evolutionary deep learning, as well as their solutions. The contribution of this chapter is to provide a systematic review of the current state of play in applying deep learning to precision medicine for viral conditions, with a particular focus on its relationship with medical virology. Researchers and practitioners can benefit from this chapter, which offers a systematic account of deep learning algorithms in healthcare contexts, including their pros and cons. By providing crucial insights into the future trajectory of deep learning in medical virology, it fosters continuous innovation in this transformative field. Ethical content on the use of AI in healthcare implementation is also addressed to ensure equitable access to these developments.