This report presents an in-depth technical literature review of deep learning methodologies and their applications in modern healthcare, with a focus on medical diagnosis, prognosis, and decision support systems. It systematically examines the evolution of artificial intelligence in medicine and provides a detailed analysis of core neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, and Transformer-based models. The study synthesizes findings from peer-reviewed research, landmark case studies, and real-world clinical deployments to evaluate the effectiveness of deep learning in key healthcare domains such as medical imaging, electronic health record (EHR) analysis, genomics, and drug discovery. Notable systems including CheXNet, Google’s LYNA, BEHRT, StageNet, DeepVariant, and DeepDTA are examined to highlight performance gains over traditional methods and unassisted human diagnosis. Beyond applications, the report critically addresses major challenges limiting large-scale clinical adoption, including data scarcity, privacy regulations (e.g., HIPAA), data quality issues, infrastructure constraints, and the black-box nature of deep learning models. Emerging solutions such as Explainable AI (XAI) techniques and Graph Neural Networks (GNNs) are discussed as pathways toward more transparent, interpretable, and trustworthy AI systems in healthcare. This work is intended to serve as a foundational technical reference for students, researchers, and practitioners seeking a structured and comprehensive understanding of deep learning in healthcare. By consolidating theoretical principles, empirical evidence, and evaluation metrics into a coherent framework, the report aims to support future research, interdisciplinary collaboration, and responsible AI integration in clinical environments.

The integration of Deep Learning (DL) into healthcare has fundamentally transformed the landscape of medical diagnosis and prognosis, offering capabilities that surpass traditional methodologies. This paper provides a comprehensive technical evaluation of neural network architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers—analyzing their specific applications in medical imaging, Electronic Health Record (EHR) analysis, and genomics. Key case studies are examined, such as CheXNet for pneumonia detection and Google’s Lymph Node Assistant (LYNA), which achieved 99% accuracy in identifying metastatic breast cancer, demonstrating the superior sensitivity and specificity of these models compared to unassisted human diagnosis. However, despite these advancements, significant barriers to clinical adoption remain, including data scarcity, privacy regulations like HIPAA, and the "black box" interpretability problem. The study consequently explores advanced methodologies such as Explainable AI (XAI) and Graph Neural Networks (GNN) as solutions to these challenges, concluding that sustainable integration requires robust measures for transparency, data privacy, and interdisciplinary collaboration.