Computational Intelligence Ethical Issues in Health Care

Over the past ten years, a significant influx of multi-modality material has contributed to the rapid growth of data analytics’ significance in health informatics. As a result, there is a growing demand for creating analytical, data-driven solutions in health informatics focused on deep learning approaches. Artificial neural networks are the foundation of the deep learning method of machine learning, which has recently gained prominence as a formidable tool for machine learning with the potential to revolutionize artificial intelligence. Rapid improvements in processing power, quick data storage, and parallelization, together with the technology’s potential for producing optimal high-level features and semantic interpretation automatically from the incoming data, have all aided in its swift acceptance. This book chapter offers a thorough, current overview of DL research in health informatics, with a critical evaluation of the method’s relative advantages, potential issues, and prospects for the future. This chapter primarily focuses on deep learning applications in translational bioinformatics, medical imaging, ubiquitous sensing, medical informatics, and public health.