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HNO

, Volume 67, Issue 5, pp 343–349 | Cite as

Künstliche Intelligenz in der Medizin – Holzweg oder Heilversprechen?

  • Daniel SonntagEmail author
Leitthema
  • 261 Downloads

Zusammenfassung

Künstliche Intelligenz (KI) hat in den letzten Jahren eine neue Reifephase erreicht und entwickelt sich zum Treiber der Digitalisierung in allen Lebensbereichen. Die KI ist eine Querschnittstechnologie, die für alle Bereiche der Medizin mit Bilddaten, Textdaten und Biodaten von großer Bedeutung ist. Es gibt keinen medizinischen Bereich, der nicht von KI beeinflusst werden wird. Dabei spielt die klinische Entscheidungsunterstützung eine wichtige Rolle. Gerade beim medizinischen Workflow-Management und bei der Vorhersage des Behandlungserfolgs bzw. Behandlungsergebnisses etablieren sich KI‑Methoden. In der Bilddiagnose und im Patientenmanagement können KI‑Systeme bereits unterstützen, aber sie können keine kritischen Entscheidungen vorschlagen. Die jeweiligen Präventions- oder Therapiemaßnahmen können mit KI‑Unterstützung sinnvoller bewertet werden, allerdings ist die Abdeckung der Krankheiten noch viel zu gering, um robuste Systeme für den klinischen Alltag zu erstellen. Der flächendeckende Einsatz setzt Fortbildungsmaßnahmen für Ärzte voraus, um die Entscheidung treffen zu können, wann auf automatische Entscheidungsunterstützung vertraut werden kann.

Schlüsselwörter

Bildauswertung, computergestützte Medizinische Informatikanwendungen Computergestützte Diagnostik Entscheidungsunterstützung Maschinelles Lernen 

Artificial intelligence in medicine—the wrong track or promise of cure?

Abstract

Artificial intelligence (AI) has attained a new level of maturity in recent years and is developing into the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all branches of medicine employing imaging as well as text and biodata. There is no field of medicine that remains unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medial workflow management and for prediction of therapeutic success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently far too low for the creation of robust systems for clinical routine. Prerequisite for the comprehensive use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

Keywords

Image interpretation, computer-assisted Medical informatics applications Diagnosis, computer-assisted  Decision making, computer-assisted  Machine learning 

Notes

Einhaltung ethischer Richtlinien

Interessenkonflikt

D. Sonntag gibt an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden vom Autor keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

Literatur

  1. 1.
    Alpaydin E (2018) Classifying multimodal data. In: The handbook of multimodal-multisensor interfaces, signal processing, architectures, and detection of emotion and cognition, Bd. 2. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  2. 2.
    Bahl M, Barzilay R, Yedidia A, Locascio N, Yu L, Lehman C (2018) High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision. Radiology 286:810–818CrossRefGoogle Scholar
  3. 3.
    Baltrusaitis T, Ahuja C, Morency L‑P (2018) Multimodal machine learning. In: The handbook of multimodal-multisensor interfaces: signal processing, architectures, and detection of emotion and cognition, Bd. 2. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  4. 4.
    Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G (2014) Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 33(7):1123–1131CrossRefGoogle Scholar
  5. 5.
    Bengio S, Deng L, Morency L‑P, Schuller B (2018) Multidisciplinary challenge topic: perspectives on predictive power of multimodal deep learning: surprises and future directions. In: The handbook of multimodal-multisensor interfaces: signal processing, architectures, and detection of emotion and cognition, Bd. 2. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  6. 6.
    Boden MA (2008) Mind as machine: a history of cognitive science. Clarendon Press, Oxford, England. (https://books.google.de/books?id=yRyETy43AdQC.)Google Scholar
  7. 7.
    Boden MA, Bryson J, Caldwell DG, Dautenhahn K, Edwards L, Kember S, Newman P, Parry V, Pegman G, Rodden T, Sorrell T, Wallis M, Whitby B, Winfield AFT (2017) Principles of robotics: regulating robots in the real world. Connect Sci 29(2):124–129.  https://doi.org/10.1080/09540091.2016.1271400 CrossRefGoogle Scholar
  8. 8.
    Burdick J, Marques O, Weinthal J, Furht B (2018) Rethinking skin lesion segmentation in a convolutional classifier. J Digit Imaging 31(4):435–440.  https://doi.org/10.1007/s10278-017-0026-y CrossRefPubMedGoogle Scholar
  9. 9.
    Choi J‑H, Kang BJ, Baek JE, Lee HS, Kim SH (2018) Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 37(3):217–225.  https://doi.org/10.14366/usg.17046 CrossRefPubMedGoogle Scholar
  10. 10.
    Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, Washington, DC  https://doi.org/10.1109/ISBI.2018.8363547 CrossRefGoogle Scholar
  11. 11.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun SJ (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115.  https://doi.org/10.1038/nature21056 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, Watanabe R, Okiyama N, Ohara K, Fujimoto M (2018) Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol 1111.  https://doi.org/10.1111/bjd.16924 CrossRefPubMedGoogle Scholar
  13. 13.
    Gelissen J, Sonntag D (2015a) Special issue on health and wellbeing. KI Künstliche Intell 29(2):111–113.  https://doi.org/10.1007/s13218-015-0360-5 CrossRefGoogle Scholar
  14. 14.
    Gelissen J, Sonntag D (2015b) Special issue on health and wellbeing. KI Künstliche Intell 29(2):111–113.  https://doi.org/10.1007/s13218-015-0360-5 CrossRefGoogle Scholar
  15. 15.
    Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt DC, Ourselin S, Cardoso MJ, Vercauteren T (2018) Niftynet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122.  https://doi.org/10.1016/j.cmpb.2018.01.025 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Handels H (2015) Medizinische Bildverarbeitung. Springer, Heidelberg, BerlinGoogle Scholar
  17. 17.
    Keren G, Mousa AE-D, Pietquin O, Zafeiriou S, Schuller B (2018) Deep learning for multisensorial and multimodal interaction. In: The handbook of multimodal-multisensor interfaces: signal processing, architectures, and detection of emotion and cognition, Bd. 2. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  18. 18.
    Langlotz CP (2006) Radlex: a new method for indexing online educational materials. Radiographics 26:1595–1597.  https://doi.org/10.1148/rg.266065168 CrossRefPubMedGoogle Scholar
  19. 19.
    Luxenburger A, Prange A, Moniri MM, Sonntag D (2016) Medicalvr: towards medical remote collaboration using virtual reality. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, UbiComp ’16. ACM, New York, S 321–324  https://doi.org/10.1145/2968219.2971392 CrossRefGoogle Scholar
  20. 20.
    Marchetti MA, Codella NC, Dusza SW, Gutman DA, Helba B, Kalloo A, Mishra N, Carrera C, Celebi ME, DeFazio JL, Jaimes N, Marghoob AA, Quigley E, Scope A, YÃl’lamos O, Halpern AC (2018) Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78(2):270–277.e1.  https://doi.org/10.1016/j.jaad.2017.08.016 CrossRefPubMedGoogle Scholar
  21. 21.
    Mejino JL, Rubin DL, Brinkley JF (2008) FMA-RadLex: an application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology. In: Proc. of AMIA symposium, S 465–469Google Scholar
  22. 22.
    Möller M, Sintek M, Biedert R, Ernst P, Dengel A, Sonntag D (2010) Representing the international classification of diseases version 10 in OWL. In: Filipe J, Dietz JLG (Hrsg) KEOD 2010—proceedings of the international conference on knowledge engineering and ontology development Valencia, 25.10.–28.10. SciTePress, Lisbon, S 50–59. ISBN 978-9-898-42529-4Google Scholar
  23. 23.
    Panagakis Y, Rudovic O, Pantic M (2018) Learning for multi-modal and context-sensitive interfaces. In: The handbook of multimodal-multisensor interfaces: signal processing, architectures, and detection of emotion and cognition, Bd. 2. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  24. 24.
    Prange A, Barz M, Sonntag D (2018) Medical 3d images in multimodal virtual reality. In: Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion, IUI’18. ACM, New York, S 19:1–19:2  https://doi.org/10.1145/3180308.3180327. ISBN 978-1-4503-5571-1CrossRefGoogle Scholar
  25. 25.
    Rizzo A, Talbot TJ (2016) Virtual reality standardized patients for clinical training. In: The digital patient. John Wiley & Sons, Hoboken, S 255–272  https://doi.org/10.1002/9781118952788.ch18. ISBN 978-1-118-95278-8CrossRefGoogle Scholar
  26. 26.
    Samwald M, Jentzsch A, Bouton C, Kallesøe C, Willighagen EL, Hajagos J, Marshall MS, Prud’hommeaux E, Hassanzadeh O, Pichler E, Stephens S (2011) Linked open drug data for pharmaceutical research and development. J Cheminform 3:19.  https://doi.org/10.1186/1758-2946-3-19 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Siekmann JH (2009) Die entwicklung der disziplin in deutschland. KI, 23(1): 47–52. http://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2009-01_page47-52_web_full.pdf. Zugegriffen: 28.1.2019
  28. 28.
    Sonntag D (2016) Medical cyber-physical systems. In: Cyber-physical system design with sensor networking technologies, control, robotics and sensors. Institution of Engineering and Technology, London, England, S 311–333Google Scholar
  29. 29.
    Sonntag D (2018) AI in germany: well-prepared and eager to do something. KI Kunstliche Intell 32(2–3):97–99.  https://doi.org/10.1007/s13218-018-0555-7 CrossRefGoogle Scholar
  30. 30.
    Sonntag D, Möller M (2010) A multimodal dialogue mashup for medical image semantics. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI ’10. ACM, New York, S 381–384  https://doi.org/10.1145/1719970.1720036. ISBN 978-1-60558-515-4CrossRefGoogle Scholar
  31. 31.
    Sonntag D, Profitlich H (2019) An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation. Artif Intell Med 93:13–28.  https://doi.org/10.1016/j.artmed.2018.08.003 CrossRefPubMedGoogle Scholar
  32. 32.
    Sonntag D, Wennerberg P, Buitelaar P, Zillner S (2009) Pillars of ontology treatment in the medical domain. J Cases Inf Techn 11(4):47–73CrossRefGoogle Scholar
  33. 33.
    Sonntag D, Schulz C, Reuschling C, Galarraga L (2012) Radspeech’s mobile dialogue system for radiologists. In: Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, IUI ’12. ACM, New York, S 317–318  https://doi.org/10.1145/2166966.2167031. ISBN 978-1-4503-1048-2CrossRefGoogle Scholar
  34. 34.
    Sonntag D, Weber M, Cavallaro A, Hammon M (2014) Integrating digital pens in breast imaging for instant knowledge acquisition. AI Mag 35(1):26–37CrossRefGoogle Scholar
  35. 35.
    Sonntag D, Tresp V, Zillner S, Cavallaro A, Hammon M, Reis A, Fasching PA, Sedlmayr M, Ganslandt T, Prokosch H, Budde K, Schmidt D, Hinrichs C, Wittenberg T, Daumke P, Oppelt PG (2016) The clinical data intelligence project—a smart data initiative. Inform Spektrum 39(4):290–300.  https://doi.org/10.1007/s00287-015-0913-x CrossRefGoogle Scholar
  36. 36.
    Stone P, Brooks R, Brynjolfsson E, Calo R, Etzioni O, Hager G, Hirschberg J, Kalyanakrishnan S, Kamar E, Kraus S, Leyton-Brown K, Parkes D, Press W, Saxenian A, Shah J, Tambe M, Teller AS (2016) Artificial intelligence and life in 2030. Technical report, one hundred year study on artificial intelligence: report of the 2015–2016 study panel. Stanford University, StanfordGoogle Scholar
  37. 37.
    Strecker H, Pfitzner K (1988) XRAY – ein prototypisches konfigurierungs-expertensystem für die automatische röntgenprüfung. KI Kunstliche Intell 2(2):4–8Google Scholar
  38. 38.
    Wang I, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) Chestx-ray8: Hospital-scale chest x‑ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. CoRR, abs/1705.02315Google Scholar
  39. 39.
    Wright A, Hickman T, McEvoy D et al (2016) Analysis of clinical decision support system malfunctions: a case series and survey. J Am Med Inform Assoc 23:1068–1076CrossRefGoogle Scholar
  40. 40.
    Yang Y, Tresp V, Wunderle M, Fasching PA (2018) Explaining therapy predictions with layer-wise relevance propagation in neural networks. 2018 IEEE International Conference on Healthcare Informatics (ICHI), S 152–162  https://doi.org/10.1109/ICHI.2018.00025 CrossRefGoogle Scholar
  41. 41.
    Zhang X, Wang S, Liu J, Tao C (2017) Computer-aided diagnosis of four common cutaneous diseases using deep learning algorithm. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, Kansas City, S 1304–1306  https://doi.org/10.1109/BIBM.2017.8217850. ISBN 978-1-5090-3050-7CrossRefGoogle Scholar

Copyright information

© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)SaarbrückenDeutschland

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