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

Wiebke Rösler¹, Michael Altenbuchinger², Bettina Baeßler³, Tim Beissbarth², Gernot Beutel⁴, Robert Bock⁵, Nikolas von Bubnoff⁶, Jan-Niklas Eckardt^{7

8}, Sebastian Foersch⁹, Chiara M L Loeffler^{7

8}, Jan Moritz Middeke^{7

8}, Martha-Lena Mueller¹⁰, Thomas Oellerich¹¹, Benjamin Risse¹², André Scherag¹³, Christoph Schliemann¹⁴, Markus Scholz¹⁵, Rainer Spang¹⁶, Christian Thielscher¹⁷, Ioannis Tsoukakis¹⁸, Jakob Nikolas Kather^{19

20

21}

Affiliations

¹ Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland.
² Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.
³ Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
⁴ Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.
⁵ IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany.
⁶ Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany.
⁷ Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
⁸ Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
⁹ Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
¹⁰ MLL Munich Leukemia Laboratory, Munich, Germany.
¹¹ Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany.
¹² Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany.
¹³ Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
¹⁴ Department of Medicine A, University Hospital Münster, Münster, Germany.
¹⁵ Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
¹⁶ Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.
¹⁷ Competence Center for Medical Economics, FOM University, Essen, Germany.
¹⁸ Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany.
¹⁹ Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
²⁰ Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
²¹ Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_nikolas.kather@tu-dresden.de.

PMID: 36920563
PMCID: PMC10374829
DOI: 10.1007/s00432-023-04667-5

Review

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

Wiebke Rösler et al. J Cancer Res Clin Oncol. 2023 Aug.

. 2023 Aug;149(10):7997-8006.

doi: 10.1007/s00432-023-04667-5. Epub 2023 Mar 15.

Authors

Affiliations

¹ Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland.
² Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.
³ Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
⁴ Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.
⁵ IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany.
⁶ Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany.
⁷ Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
⁸ Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
⁹ Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
¹⁰ MLL Munich Leukemia Laboratory, Munich, Germany.
¹¹ Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany.
¹² Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany.
¹³ Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
¹⁴ Department of Medicine A, University Hospital Münster, Münster, Germany.
¹⁵ Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
¹⁶ Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.
¹⁷ Competence Center for Medical Economics, FOM University, Essen, Germany.
¹⁸ Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany.
¹⁹ Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
²⁰ Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
²¹ Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_nikolas.kather@tu-dresden.de.

PMID: 36920563
PMCID: PMC10374829
DOI: 10.1007/s00432-023-04667-5

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.

Keywords: Artificial intelligence; Computer vision; Digital health; Large language models; Machine learning.

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Conflict of interest statement

JNK reports consulting services for Owkin, France, Panakeia, UK, and DoMore Diagnostics, Norway and has received honoraria for lectures by MSD, Eisai, and Fresenius. WR reports travel support from Janssen, AstraZeneca and Amgen, honoraria for lectures from AstraZeneca and received a grant from Novartis. NvB received honoraria from Takeda and travel support from Janssen-Cilag.MS receives funding from Pfizer Inc. for a project not related to the topic of this paper.BR has no conflicts of interest. JMM reports consulting services for Janssen, Roche, Gilead, Abbvie, Jazz, Pfizer, Astellas, Novartis and funding of scientific projects from Janssen, Jazz, Novartis and Astellas. The other authors do not report any conflicts of interest.

Figures

**Fig. 1**
Artificial intelligence in oncology. A A simplified visualization of key methodological areas in oncological data science with example methods. B An expert-based definition of key fields of application of AI in oncology. Abbreviation: *NLP* natural language processing, *RWD* real-world data, *HER* electronic health record, *PRO* patient-reported outcomes

See this image and copyright information in PMC

References

1. Aerts HJWL et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006 - PMC - PubMed
1. Alexander A, Jiang A, Ferreira C, Zurkiya D (2020) An intelligent future for medical imaging: a market outlook on artificial intelligence for medical imaging. J Am Coll Radiol 17:165–170 - PubMed
1. Andaur NC et al (2023) Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models. J Clin Epidemiol 154:8–22 - PubMed
1. Araki K et al (2022) Developing artificial intelligence models for extracting oncologic outcomes from japanese electronic health records. Adv Ther. 10.1007/s12325-022-02397-7 - PMC - PubMed
1. Balasubramaniam V, 2021 Artificial intelligence algorithm with SVM classification using dermascopic images for melanoma diagnosis. March.3: 34–42

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources