Literature DB >> 35928184

Generative adversarial networks and synthetic patient data: current challenges and future perspectives.

Anmol Arora1, Ananya Arora1.   

Abstract

Artificial intelligence (AI) has been heralded as one of the key technological innovations of the 21st century. Within healthcare, much attention has been placed upon the ability of deductive AI systems to analyse large datasets to find patterns that would be unfeasible to program. Generative AI, including generative adversarial networks, are a newer type of machine learning that functions to create fake data after learning the properties of real data. Artificially generated patient data has the potential to revolutionise clinical research and protect patient privacy. Using novel techniques, it is increasingly possible to fully anonymise datasets to the point where no datapoint is traceable to any real individual. This can be used to expand and balance datasets as well as to replace the use of real patient data in certain contexts. This paper focuses upon three key uses of synthetic data: clinical research, data privacy and medical education. We also highlight ethical and practical concerns that require consideration. © Royal College of Physicians 2022 All rights reserved.

Entities:  

Keywords:  confidentiality; ethics; generative adversarial networks; legal frameworks; machine learning

Year:  2022        PMID: 35928184      PMCID: PMC9345230          DOI: 10.7861/fhj.2022-0013

Source DB:  PubMed          Journal:  Future Healthc J        ISSN: 2514-6645


  14 in total

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2.  Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation.

Authors:  Sébastien Marcel; José Del R Millán
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2007-04       Impact factor: 6.226

Review 3.  Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

Authors:  Jiwoong J Jeong; Amara Tariq; Tobiloba Adejumo; Hari Trivedi; Judy W Gichoya; Imon Banerjee
Journal:  J Digit Imaging       Date:  2022-01-12       Impact factor: 4.056

4.  HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis.

Authors:  Xiaodan Xing; Javier Del Ser; Yinzhe Wu; Yang Li; Jun Xia; Xu Lei; David Firmin; Peter Gatehouse; Guang Yang
Journal:  IEEE J Biomed Health Inform       Date:  2022-03-15       Impact factor: 5.772

Review 5.  The medical algorithmic audit.

Authors:  Xiaoxuan Liu; Ben Glocker; Melissa M McCradden; Marzyeh Ghassemi; Alastair K Denniston; Lauren Oakden-Rayner
Journal:  Lancet Digit Health       Date:  2022-04-05

6.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Authors:  Ryan Poplin; Avinash V Varadarajan; Katy Blumer; Yun Liu; Michael V McConnell; Greg S Corrado; Lily Peng; Dale R Webster
Journal:  Nat Biomed Eng       Date:  2018-02-19       Impact factor: 25.671

7.  Synthetic data in machine learning for medicine and healthcare.

Authors:  Richard J Chen; Ming Y Lu; Tiffany Y Chen; Drew F K Williamson; Faisal Mahmood
Journal:  Nat Biomed Eng       Date:  2021-06       Impact factor: 29.234

8.  DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis.

Authors:  Chengjia Wang; Guang Yang; Giorgos Papanastasiou; Sotirios A Tsaftaris; David E Newby; Calum Gray; Gillian Macnaught; Tom J MacGillivray
Journal:  Inf Fusion       Date:  2021-03       Impact factor: 12.975

Review 9.  Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

Authors:  Xiaoxuan Liu; Samantha Cruz Rivera; David Moher; Melanie J Calvert; Alastair K Denniston
Journal:  Nat Med       Date:  2020-09-09       Impact factor: 87.241

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