Healthcare is transforming through adoption of information technologies (IT) and digitalization. Machine learning (ML) and artificial intelligence (AI) are two of the IT technologies that are leading this transformation. In this chapter we focus on Deep Learning (DL), a subfield of ML that relies on deep artificial neural networks to deliver breakthroughs in long-standing AI problems. DL is about working with high-dimensional data (e.g., images, speech recording, natural language) and learning efficient representations that allow for building successful models. We present a structured overview of DL methods applied to healthcare problems based on their suitability of the different technologies to the available modalities of healthcare data. This data-centric perspective reflects the data-driven nature of DL methods and allows side-by-side comparison with different domains in healthcare. Challenges, in broad adoption of DL, are commonly related to some of its main drawbacks, particularly lack of interpretability and transparency. We discuss the drawbacks and limitations of DL technology that specifically come to light in the domain of healthcare. We also address the need for a considerable amount of data and annotations to successfully build these models that can be a particularly expensive and time-consuming effort. Overall, the chapter offers insights into existing applications of DL to healthcare on their suitability for specific types of data and their limitations.
|Title of host publication||Data Science for Healthcare|
|Subtitle of host publication||Methodologies and Applications|
|Editors||Sergio Consoli, Diego Reforgiato Recupero, Milan Petković|
|Place of Publication||Cham|
|Number of pages||42|
|Publication status||Published - 1 Jan 2019|