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Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung

Translated title of the contribution: Artificial intelligence and machine learning in oncologic imaging
  • Jens Kleesiek
  • , Jacob M. Murray
  • , Christian Strack
  • , Sebastian Prinz
  • , Georgios Kaissis
  • , Rickmer Braren
  • German Cancer Research Center
  • University Hospital of Essen
  • Heidelberg University
  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

Abstract

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers—in collaboration with humans or alone—have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

Translated title of the contributionArtificial intelligence and machine learning in oncologic imaging
Original languageGerman
Pages (from-to)649-658
Number of pages10
JournalPathologe
Volume41
Issue number6
DOIs
StatePublished - 1 Nov 2020

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