AI for Smart Healthcare

Abstract

Healthcare is a domain where AI, and specifically, deep learning, has shown enormous promise. However, this domain also presents unique challenges in terms of safety, interpretability, and clinical integration. In this chapter, we review core neural architectures-convolutional networks, recurrent networks, graph neural networks, autoencoders, and generative adversarial networks-and discuss their suitability for different biomedical data types. We then present a detailed case study on liver disease screening, where densely connected deep neural networks were trained on liver function test indicators to classify disease status. The chapter draws lessons about the strengths and limitations of deep learning in healthcare, emphasizing issues of scalability, interpretability, and generalization across populations. We conclude by reflecting on how to bridge research innovations with real-world deployment in clinical settings, ensuring both effectiveness and trustworthiness.