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Künstliche Intelligenz in der Kardiologie

Relevanz, aktuelle Anwendungen und nächste Schritte

Artificial intelligence in cardiology

Relevance, current applications, and future developments

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Zusammenfassung

Big Data und Anwendungen der künstlichen Intelligenz (KI), wie maschinelles Lernen oder Deep Learning, werden die Gesundheitsversorgung zukünftig bereichern und an Bedeutung gewinnen. Sie haben u. a. das Potenzial, unnötige Untersuchungen sowie Diagnose- und Therapiefehler zu vermeiden und eine verbesserte, frühzeitige und beschleunigte Entscheidungsfindung zu ermöglichen. Die Autoren geben in dem Artikel einen Überblick über aktuelle KI-basierte Anwendungen in der Kardiologie. Die Beispiele beschreiben innovative Lösungen zur Risikobewertung, Diagnosestellung und Therapieunterstützung bis hin zum Selbstmanagement der Patienten. Big Data und KI dienen dabei als Basis für eine effiziente, prädiktive, präventive und personalisierte Medizin. Allerdings zeigen die Beispiele auch, dass es weiterer Forschungen bedarf, um die Lösungen im Sinne der Patienten und Ärzteschaft weiter zu entwickeln, die Effektivität und den Nutzen in der Gesundheitsversorgung zu zeigen sowie rechtliche und ethische Standards zu etablieren.

Abstract

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as a basis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

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Notes

  1. Aus Gründen der besseren Lesbarkeit wird auf die gleichzeitige Verwendung männlicher und weiblicher Sprachformen verzichtet. Sämtliche Personenbezeichnungen gelten gleichermaßen für Frauen, Männer und andere Geschlechter.

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Authors and Affiliations

  1. Deutsche Stiftung für chronisch Kranke, Berlin, Deutschland

    Bettina Zippel-Schultz

  2. Lehrstuhl für Technologiemanagement, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland

    Carsten Schultz

  3. Medizinische Klinik I – Kardiologie, Angiologie und Internistische Intensivmedizin, Uniklinik RWTH Aachen, Aachen, Deutschland

    Dirk Müller-Wieland

  4. Klinik für Kardiologie, Herz- und Gefässzentrum Bad Bevensen, Bad Bevensen, Deutschland

    Andrew B. Remppis

  5. Medizinische Klinik Nauen, Schwerpunkt Kardiologie, Havelland Kliniken, Nauen, Deutschland

    Martin Stockburger

  6. Medizinische Klinik 1, St.-Marien-Hospital Lünen, Lünen, Deutschland

    Christian Perings

  7. Deutsche Stiftung für chronisch Kranke, Fürth, Deutschland

    Thomas M. Helms

  8. Peri Cor Arbeitsgruppe Kardiologie/Ass. UCSF, Hamburg, Deutschland

    Thomas M. Helms

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B. Zippel-Schultz, C. Schultz, D. Müller-Wieland, A.B. Remppis, M. Stockburger, C. Perings und T. M. Helms geben an, dass kein Interessenkonflikt besteht.

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

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Zippel-Schultz, B., Schultz, C., Müller-Wieland, D. et al. Künstliche Intelligenz in der Kardiologie. Herzschr Elektrophys 32, 89–98 (2021). https://doi.org/10.1007/s00399-020-00735-2

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