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Seminar paper from the year 2019 in the subject Health - Miscellaneous, grade: 2,00, Management Center Innsbruck, language: English, abstract: The global challenge against non-communicable diseases gained transnational attention over the last years. Statistics illustrate, that approximately 70% of all deaths worldwide can be traced back to chronic diseases. On the other hand, artificial intelligence is on the rise as the number of implemented software featuring machine-learning skills increased tremendously in the last years. Therefore, the purpose of this literature review is to combine those…mehr

Produktbeschreibung
Seminar paper from the year 2019 in the subject Health - Miscellaneous, grade: 2,00, Management Center Innsbruck, language: English, abstract: The global challenge against non-communicable diseases gained transnational attention over the last years. Statistics illustrate, that approximately 70% of all deaths worldwide can be traced back to chronic diseases. On the other hand, artificial intelligence is on the rise as the number of implemented software featuring machine-learning skills increased tremendously in the last years. Therefore, the purpose of this literature review is to combine those aspects in order to create synergies of the link between existing approaches with the aim to face non-communicable diseases and artificial intelligence. The review focusses firstly specifically only on the most prevalent NCDs and their characteristics before analysing the rise of machine-learning software from the very beginning. In a next step, those two fields will be combined with the aim to point out both benefits and risks when implementing AI in the healthcare sector. The outcome was, that the use of such software would definitely cut a three-digit number of billions of Euros of spending on healthcare due to the possibility to early-detect diseases and the hereby arising possibility to treat patients in a more efficient way as until now. On the other hand, the biggest risk will be, that artificial intelligence works on huge datasets that need to be set up first - during the setting up process of such data, failures can take place which would have a grave impact on further use.

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