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Künstliche Intelligenz in der Intensivmedizin

Artificial intelligence in intensive care medicine

  • Leitthema
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Zusammenfassung

Die Integration von künstlicher Intelligenz (KI) in die Intensivmedizin zeigt in neuesten Studien besonders in den Bereichen der prädiktiven Analytik, der Früherkennung von Komplikationen und der Entwicklung von Entscheidungsunterstützungssystemen beachtliche Fortschritte. Die Hauptherausforderungen bestehen weiterhin in der Verfügbarkeit und Qualität der Daten, der Reduzierung von Verzerrungen und der Notwendigkeit erklärbarer Ergebnisse von Algorithmen und Modellen. Methoden zur Erklärung dieser Systeme sind essenziell, um Vertrauen, Verständnis und ethische Überlegungen bei Gesundheitsfachkräften und Patienten zu stärken. Eine fundierte Ausbildung des medizinischen Personals in KI-Prinzipien, Terminologie, ethischen Überlegungen und in der praktischen Anwendung ist für den erfolgreichen Einsatz von KI entscheidend. Die sorgfältige Bewertung der Auswirkungen von KI auf Patientenautonomie und Datenschutz ist unabdingbar für deren verantwortungsvolle Nutzung in der Intensivmedizin. Hierbei ist die Balance zwischen ethischen und praktischen Erwägungen zu wahren, um eine patientenzentrierte Versorgung bei gleichzeitiger Einhaltung von Datenschutzbestimmungen zu gewährleisten. Eine synergistische Zusammenarbeit zwischen Klinikern, KI-Ingenieuren und Regulierungsbehörden ist entscheidend, um das volle Potenzial der KI in der Intensivmedizin zu realisieren und ihre positive Wirkung auf die Patientenversorgung zu maximieren. Zukünftige Forschungs- und Entwicklungsanstrengungen sollten sich auf die Verbesserung von KI-Modellen für Echtzeitvorhersagen, die Steigerung der Genauigkeit und des Nutzens KI-basierter Closed-loop-Systeme sowie die Überwindung ethischer, technischer und regulatorischer Herausforderungen, insbesondere bei generativen KI-Systemen, fokussieren.

Abstract

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

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  1. Zentrum für Präventivmedizin und Digitale Gesundheit, Medizinische Fakultät Mannheim der Universität Heidelberg, Theodor-Kutzer-Ufer 1–3, 68167, Mannheim, Deutschland

    André Baumgart

  2. Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland

    Grietje Beck &  David Ghezel-Ahmadi

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Baumgart, A., Beck, G. & Ghezel-Ahmadi, D. Künstliche Intelligenz in der Intensivmedizin. Med Klin Intensivmed Notfmed 119, 189–198 (2024). https://doi.org/10.1007/s00063-024-01117-z

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