Künstliche Intelligenz und maschinelles Lernen in der intensivmedizinischen Forschung und klinischen Anwendung

Peine A, Lütge C, Poszler F, Celi L, Schöffski O, Marx G, Martin L (2020)


Publication Type: Journal article, Review article

Publication year: 2020

Journal

Book Volume: 61

Pages Range: 372-384

Journal Issue: 9

DOI: 10.19224/ai2020.372

Abstract

Hardly any other development is predicted to have a greater impact on our daily working life than artificial intelligence (AI). A popular field of application of artificial intelligence is the so-called “machine learning”, the discipline that deals with the generation of computerised knowledge from experience through self-adaptive algoriths. Especially the high practical relevance, for example, in the field of pattern recognition and prediction, makes machine learning a preferred field of application in the medical domain. Especially in intensive care medicine, characterised by an exceptionally high data density and widespread computer-based data acquisition routines, machine learning has recently gained relevant influence in a scientific context as well. The data density of intensive care medicine, resulting from the steadily increasing number of connected devices and data streams, makes the application of AI a preferred field of application in research and development. This opens up new horizons for practice. Thus, after validation, AI-based algorithms in future will not only be able to influence the behaviour of the professions involved, but also directly influence the treatment of patients.

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How to cite

APA:

Peine, A., Lütge, C., Poszler, F., Celi, L., Schöffski, O., Marx, G., & Martin, L. (2020). Künstliche Intelligenz und maschinelles Lernen in der intensivmedizinischen Forschung und klinischen Anwendung. Anästhesiologie & Intensivmedizin, 61(9), 372-384. https://doi.org/10.19224/ai2020.372

MLA:

Peine, A., et al. "Künstliche Intelligenz und maschinelles Lernen in der intensivmedizinischen Forschung und klinischen Anwendung." Anästhesiologie & Intensivmedizin 61.9 (2020): 372-384.

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