The Role of Neural Networks in Smart Computing Applications for Healthcare

Abstract

Healthcare systems are increasingly adopting smart computing and artificial intelligence to improve diagnostics, treatment planning, and patient outcomes. Neural networks, as a subset of AI, have emerged as a powerful computational paradigm capable of extracting patterns from complex medical data such as radiological images, genomic sequences, and patient histories. This paper provides an in-depth exploration of how neural network architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), contribute to healthcare innovation. Case studies are examined where neural networks outperform traditional methods in early disease detection, medical imaging classification, drug discovery, and personalized medicine. Additionally, the research evaluates challenges such as data privacy, algorithmic bias, interpretability, and scalability. The paper emphasizes that integrating neural networks with smart computing infrastructure not only enhances efficiency but also ensures real-time, accurate, and patient-centered healthcare solutions.

KEYWORDS: Neural Networks; Smart Computing; Healthcare AI; Deep Learning; Medical Applications