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Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen

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Künstliche Intelligenz im Gesundheitswesen

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Zusammenfassung

Der große Erfolg moderner, bildbasierter KI-Methoden und das damit einhergehende Interesse für die Anwendung von KI in kritischen Entscheidungsprozessen führte zu einem Anstieg der Bemühungen, intelligente Systeme transparent und erklärbar zu gestalten. Besonders im medizinischen Kontext, wo computergestützte Entscheidungen direkten Einfluss auf die Behandlung und das Wohlsein von Patienten haben können, ist Transparenz für den sicheren Übergang von Forschung in die Praxis von höchster Wichtigkeit. Dieser Beitrag beschäftigt sich mit dem aktuellen Stand moderner Methoden zur Erklärung und Interpretation von Deep-Learning-basierten KI-Algorithmen in Anwendungen der medizinischen Forschung und Diagnose von Krankheiten. Zunächst werden erste bemerkenswerte Erfolge im Einsatz erklärbarer KI zur Validierung bekannter und Exploration potenzieller Biomarker sowie Methoden zur nachträglichen Korrektur von KI-Modellen aufgezeigt. Im Anschluss werden einige verbleibende Herausforderungen, die der Anwendung von KI als klinische Entscheidungshilfe im Weg stehen, kritisch diskutiert und Empfehlungen für die Ausrichtung zukünftiger Forschung ausgesprochen.

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  1. Smarte Daten & Wissensdienste, DFKI GmbH, Kaiserslautern, Deutschland

    Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel & Sheraz Ahmed

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  1. Adriano Lucieri
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  2. Muhammad Naseer Bajwa
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  3. Andreas Dengel
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  4. Sheraz Ahmed
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Correspondence to Adriano Lucieri or Muhammad Naseer Bajwa .

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  1. Hochschule für angewandte Wissenschaften Neu-Ulm, Fakultät Gesundheitsmanagement, Neu-Ulm, Deutschland

    Mario A. Pfannstiel

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Lucieri, A., Bajwa, M.N., Dengel, A., Ahmed, S. (2022). Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen. In: Pfannstiel, M.A. (eds) Künstliche Intelligenz im Gesundheitswesen. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-33597-7_35

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