Deep learning-Prediction

Chris Al Gerges*, Melle B. Vessies, Rutger R. van de Leur, René van Es

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Deep learning is a subfield of artificial intelligence (AI) that is concerned with developing large and complex neural networks for various tasks. As of today, there exists a wide variety of DL models yielding promising results in many subfields of AI, such as computer vision (CV) and natural language processing (NLP). In this chapter, we provide an overview of deep learning, elaborating on some common model architectures. Furthermore, we describe the advantages and disadvantages of deep learning compared to machine learning. Afterwards, we discuss the application of deep learning models in various clinical tasks, focusing on clinical imaging, electronic health records and genomics. We also provide a brief overview of prediction tasks in deep learning. The final section of this chapter discusses the limitations and challenges of deploying deep learning models in healthcare and medicine, focusing on the lack of explainability in deep learning models.

Original languageEnglish
Title of host publicationClinical Applications of Artificial Intelligence in Real-World Data
EditorsFolkert W. Asselbergs, Spiros Denaxas, Daniel L. Oberski, Jason H. Moore
Place of PublicationCham
PublisherSpringer
Pages189-202
Number of pages14
Edition1
ISBN (Electronic)9783031366789
ISBN (Print)9783031366772
DOIs
Publication statusPublished - 5 Nov 2023

Keywords

  • Classification
  • Clinical imaging
  • Deep learning
  • Electronic health records
  • Explainable AI (XAI)
  • Machine learning
  • Regression
  • Segmentation

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