Research on Development Strategies of Deep Learning in Medical Applications

Deep learning has emerged as a transformative force in healthcare, revolutionizing diagnostics, treatment planning, and drug discovery through its ability to extract complex patterns from high-dimensional data. This paper provides a comprehensive review of Deep learning applications in medical imaging, genomics, and clinical decision-making, emphasizing technical methodologies and real-world impact. In medical imaging, convolutional neural networks achieve radiologist-level accuracy in detecting diseases such as pneumonia and glioblastoma, while attention mechanisms enhance localization of abnormalities. In genomics, transformer-based models like AlphaFold predict protein structures with atomic precision, accelerating drug development. Clinical decision support systems, leveraging recurrent neural networks, predict acute kidney injury and optimize chemotherapy regimens, improving patient outcomes. Despite these advancements, challenges remain, including data bias, model interpretability, and regulatory hurdles. The study evaluates strategies such as federated learning for privacy-preserving data sharing and explainable AI for transparency. Emerging trends like hybrid models and multimodal learning are highlighted as key drivers for future innovation. This paper underscores the need for interdisciplinary collaboration and robust validation frameworks to ensure ethical and equitable deployment of Deep learning in global healthcare.