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An Experimental Healthcare System: Essence and Challenges
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Author(s): Miroslav M. Bojović (School of Electrical Engineering, University of Belgrade, Serbia), Veljko Milutinović (School of Electrical Engineering, University of Belgrade, Serbia), Dragan Bojić (School of Electrical Engineering, University of Belgrade, Serbia)and Nenad Korolija (School of Electrical Engineering, University of Belgrade, Serbia)
Copyright: 2021
Pages: 8
Source title:
Handbook of Research on Methodologies and Applications of Supercomputing
Source Author(s)/Editor(s): Veljko Milutinović (Indiana University, Bloomington, USA)and Miloš Kotlar (University of Belgrade, Serbia)
DOI: 10.4018/978-1-7998-7156-9.ch019
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Abstract
Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.
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