An overview and a roadmap for artificial intelligence in hematology and oncology

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Wiebke Rösler - , University of Zurich (Author)
  • Michael Altenbuchinger - , University of Göttingen (Author)
  • Bettina Baeßler - , University of Würzburg (Author)
  • Tim Beissbarth - , University of Göttingen (Author)
  • Gernot Beutel - , Hannover Medical School (MHH) (Author)
  • Robert Bock - , Institute for Microelectronics and Mechatronics Systems GmbH (Author)
  • Nikolas von Bubnoff - , Universitätsklinikum Schleswig-Holstein - Campus Lübeck (Author)
  • Jan Niklas Eckardt - , Department of internal Medicine I, TUD Dresden University of Technology (Author)
  • Sebastian Foersch - , Johannes Gutenberg University Mainz (Author)
  • Chiara M.L. Loeffler - , Else Kröner Fresenius Center for Digital Health, Department of internal Medicine I (Author)
  • Jan Moritz Middeke - , Department of internal Medicine I, TUD Dresden University of Technology (Author)
  • Martha Lena Mueller - , Munich Leukemia Laboratory (Author)
  • Thomas Oellerich - , University Hospital Frankfurt (Author)
  • Benjamin Risse - , University of Münster (Author)
  • André Scherag - , Friedrich Schiller University Jena (Author)
  • Christoph Schliemann - , University of Münster (Author)
  • Markus Scholz - , Leipzig University (Author)
  • Rainer Spang - , University of Regensburg (Author)
  • Christian Thielscher - , FOM University of Applied Sciences (Author)
  • Ioannis Tsoukakis - , Sana Clinics Group (Author)
  • Jakob Nikolas Kather - , Department of internal Medicine I, Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Heidelberg University  (Author)

Abstract

Background: Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. Methods: In this article, we provide an expert-based consensus statement by the joint Working Group on “Artificial Intelligence in Hematology and Oncology” by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. Results: First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. Conclusion: Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

Details

Original languageEnglish
Pages (from-to)7997-8006
Number of pages10
JournalJournal of cancer research and clinical oncology
Volume149
Issue number10
Publication statusPublished - Aug 2023
Peer-reviewedYes

External IDs

PubMed 36920563

Keywords

Sustainable Development Goals

ASJC Scopus subject areas

Keywords

  • Artificial intelligence, Computer vision, Digital health, Large language models, Machine learning, Medical Oncology, Hematology, Humans, Artificial Intelligence, Forecasting