Literature DB >> 34351439

[Digital transformation in urology-opportunity, risk or necessity?]

T Loch1, U Witzsch2, G Reis3.   

Abstract

Ultimately, new (digital) techniques and artificial intelligence (AI) applications are changing the working environment in urology. This can be an opportunity for further development, but also a change which is not desired. Adjustments to work processes may be necessary. So-called disruptive processes lead to fundamental changes. In the context of the digital transformation, our way of working is changing. Classic hierarchies, working hours, and working environments are dissolving in favor of creative and flexible working models and corporate structures. Clinics and practices in urology must prepare themselves for changing requirements and be able to provide answers.
© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

Keywords:  Artificial intelligence; Data security; Healthcare; Precision medicine; Telemedicine

Year:  2021        PMID: 34351439     DOI: 10.1007/s00120-021-01610-9

Source DB:  PubMed          Journal:  Urologe A        ISSN: 0340-2592            Impact factor:   0.639


  7 in total

Review 1.  Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

Authors:  Rodrigo Suarez-Ibarrola; Simon Hein; Gerd Reis; Christian Gratzke; Arkadiusz Miernik
Journal:  World J Urol       Date:  2019-11-05       Impact factor: 4.226

Review 2.  Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study.

Authors:  Milap Shah; Nithesh Naik; Bhaskar K Somani; B M Zeeshan Hameed
Journal:  Turk J Urol       Date:  2020-05-27

Review 3.  Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.

Authors:  B M Zeeshan Hameed; Aiswarya V L S Dhavileswarapu; Syed Zahid Raza; Hadis Karimi; Harneet Singh Khanuja; Dasharathraj K Shetty; Sufyan Ibrahim; Milap J Shah; Nithesh Naik; Rahul Paul; Bhavan Prasad Rai; Bhaskar K Somani
Journal:  J Clin Med       Date:  2021-04-26       Impact factor: 4.241

Review 4.  Artificial intelligence in healthcare: past, present and future.

Authors:  Fei Jiang; Yong Jiang; Hui Zhi; Yi Dong; Hao Li; Sufeng Ma; Yilong Wang; Qiang Dong; Haipeng Shen; Yongjun Wang
Journal:  Stroke Vasc Neurol       Date:  2017-06-21

5.  Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens.

Authors:  Kunal Nagpal; Davis Foote; Fraser Tan; Yun Liu; Po-Hsuan Cameron Chen; David F Steiner; Naren Manoj; Niels Olson; Jenny L Smith; Arash Mohtashamian; Brandon Peterson; Mahul B Amin; Andrew J Evans; Joan W Sweet; Carol Cheung; Theodorus van der Kwast; Ankur R Sangoi; Ming Zhou; Robert Allan; Peter A Humphrey; Jason D Hipp; Krishna Gadepalli; Greg S Corrado; Lily H Peng; Martin C Stumpe; Craig H Mermel
Journal:  JAMA Oncol       Date:  2020-09-01       Impact factor: 31.777

6.  Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

Authors:  Eirini Arvaniti; Kim S Fricker; Michael Moret; Niels Rupp; Thomas Hermanns; Christian Fankhauser; Norbert Wey; Peter J Wild; Jan H Rüschoff; Manfred Claassen
Journal:  Sci Rep       Date:  2018-08-13       Impact factor: 4.379

7.  Implementation and Outcomes of Virtual Care Across a Tertiary Cancer Center During COVID-19.

Authors:  Alejandro Berlin; Mike Lovas; Tran Truong; Sheena Melwani; Justin Liu; Zhihui Amy Liu; Adam Badzynski; Mary Beth Carpenter; Carl Virtanen; Lyndon Morley; Onil Bhattacharyya; Marnie Escaf; Lesley Moody; Avi Goldfarb; Luke Brzozowski; Joseph Cafazzo; Melvin L K Chua; A Keith Stewart; Monika K Krzyzanowska
Journal:  JAMA Oncol       Date:  2021-04-01       Impact factor: 31.777

  7 in total

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