In Journal of cancer research and clinical oncology
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.
Rösler Wiebke, Altenbuchinger Michael, Baeßler Bettina, Beissbarth Tim, Beutel Gernot, Bock Robert, von Bubnoff Nikolas, Eckardt Jan-Niklas, Foersch Sebastian, Loeffler Chiara M L, Middeke Jan Moritz, Mueller Martha-Lena, Oellerich Thomas, Risse Benjamin, Scherag André, Schliemann Christoph, Scholz Markus, Spang Rainer, Thielscher Christian, Tsoukakis Ioannis, Kather Jakob Nikolas
2023-Mar-15
Artificial intelligence, Computer vision, Digital health, Large language models, Machine learning
