Roesler, Wiebke and Altenbuchinger, Michael and Baessler, Bettina and Beissbarth, Tim and Beutel, Gernot and Bock, Robert and von Bubnoff, Nikolas and Eckardt, Jan-Niklas and Foersch, Sebastian and Loeffler, Chiara M. L. and Middeke, Jan Moritz and Mueller, Martha-Lena and Oellerich, Thomas and Risse, Benjamin and Scherag, Andre and Schliemann, Christoph and Scholz, Markus and Spang, Rainer and Thielscher, Christian and Tsoukakis, Ioannis and Kather, Jakob Nikolas (2023) An overview and a roadmap for artificial intelligence in hematology and oncology. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, 149. pp. 7997-8006. ISSN 0171-5216, 1432-1335
Full text not available from this repository. (Request a copy)Abstract
BackgroundArtificial 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.MethodsIn 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.ResultsFirst, 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.ConclusionThus, 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | PREDICTION; VALIDATION; MEDICINE; RECORDS; FUTURE; Artificial intelligence; Machine learning; Digital health; Large language models; Computer vision |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Informatics and Data Science > Department Computational Life Science > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 May 2024 10:28 |
| Last Modified: | 08 May 2024 10:28 |
| URI: | https://pred.uni-regensburg.de/id/eprint/60842 |
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