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ISSN: 1948-593X

Journal of Bioanalysis & Biomedicine
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  • Research Article   
  • J Bioanal Biomed 2017 9: 283,
  • DOI: 10.4172/1948-593X.1000194

Medical 4.0: Medical Data Ready for Deep and Machine Learning

Natalia Labuda1, Tomasz Lepa2, Marek Labuda2 and Karol Kozak1,3,4*
1Clinic for Neurology, Carl Gustav Carus Campus, Technische Universität Dresden, , Fetscherstr 74, D-01307 Dresden, Germany
2Digitial 4.0 Sp.z.o.o, , ul. Smęgorzyńska 26, 80-298 Gdańsk, Poland
3Fraunhofer IWS, Winterbergstr 28, , D-01277 Dresden, Germany
4Medical University Wroclaw, Wybrzeże L Pasteura 1, , PL-50-367 Wrocław, Poland
*Corresponding Author : Karol Kozak, Clinic for Neurology, Carl Gustav Carus Campus, Technische Universität Dresden, Fetscherstr, Dresden, Germany, Tel: 035183391-3717, Email: [email protected]

Received Date: Nov 14, 2017 / Accepted Date: Nov 28, 2017 / Published Date: Dec 06, 2017

Abstract

Digitization is not an end in itself, but rather a means to an end. Ultimately, the point is to continue improving care provision in Europe, creating more efficient medical processes and achieving additional benefits for health care system. Medicine represents the next frontier for the Internet of Things: Four digital evolutions. Doodle is a famous process and database and works best for coordinating teams of people. Doodle radically simplifies the process of collecting data for scheduling events, meetings, appointments. However, the need to collect and share available scientific data on single PC or server from multiple project partners is a challenging process. This manuscript presents Medical 4.0, a doodle alternative for scientific data, presenting how radically the platform contributes to the four digital evolutions in medicine.

Keywords: Digitization; Doodle; Medicine

Citation: Labuda N, Lepa T, Labuda M, Kozak K (2017) Medical 4.0: Medical Data Ready for Deep and Machine Learning. J Bioanal Biomed 9: 283-287. Doi: 10.4172/1948-593X.1000194

Copyright: © 2017 Labuda N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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