Künstliche Intelligenz in der Intensivmedizin: Leitthema
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...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Deutsch |
| Veröffentlicht: |
April 2024
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| In: |
Medizinische Klinik, Intensivmedizin und Notfallmedizin
Year: 2024, Jahrgang: 119, Heft: 3, Pages: 189-198 |
| ISSN: | 2193-6226 |
| DOI: | 10.1007/s00063-024-01117-z |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00063-024-01117-z Verlag, lizenzpflichtig, Volltext: http://link.springer.com/article/10.1007/s00063-024-01117-z 
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| Verfasserangaben: | André Baumgart, Grietje Beck, David Ghezel-Ahmadi |
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| 520 | |a 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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