Jens Kleesiek1,2, Jacob M Murray3,4, Christian Strack3,4, Georgios Kaissis5, Rickmer Braren5,6. 1. AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de. 2. German Cancer Consortium (DKTK), Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de. 3. AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. 4. Universität Heidelberg, Heidelberg, Deutschland. 5. Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland. 6. German Cancer Consortium (DKTK), Heidelberg, Deutschland.
Abstract
BACKGROUND: The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of the individual patient. It is therefore important to deal with this topic and to develop a basic understanding of it. OBJECTIVES: This article gives an overview of the exciting and dynamic field of machine learning and serves as an introduction to some methods primarily from the realm of supervised learning. In addition to definitions and simple examples, limitations are discussed. CONCLUSIONS: The basic principles behind the methods are simple. Nevertheless, due to their high dimensional nature, the factors influencing the results are often difficult or impossible to understand by humans. In order to build confidence in the new technologies and to guarantee their safe application, we need explainable algorithms and prospective effectiveness studies.
BACKGROUND: The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of the individual patient. It is therefore important to deal with this topic and to develop a basic understanding of it. OBJECTIVES: This article gives an overview of the exciting and dynamic field of machine learning and serves as an introduction to some methods primarily from the realm of supervised learning. In addition to definitions and simple examples, limitations are discussed. CONCLUSIONS: The basic principles behind the methods are simple. Nevertheless, due to their high dimensional nature, the factors influencing the results are often difficult or impossible to understand by humans. In order to build confidence in the new technologies and to guarantee their safe application, we need explainable algorithms and prospective effectiveness studies.
Entities:
Keywords:
Artificial neural networks; Deep learning; Digital literacy; Machine learning; New technologies