Nazish Khalid
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, Nazish; Qayyum, Adnan; Bilal, Muhammad; Al-Fuqaha, Ala; Qadir, Junaid
Authors
Adnan Qayyum
Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application
Ala Al-Fuqaha
Junaid Qadir
Abstract
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
Citation
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, Article 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
Journal Article Type | Review |
---|---|
Acceptance Date | Mar 30, 2023 |
Online Publication Date | Apr 5, 2023 |
Publication Date | May 1, 2023 |
Deposit Date | May 2, 2023 |
Publicly Available Date | May 2, 2023 |
Journal | Computers in Biology and Medicine |
Print ISSN | 0010-4825 |
Electronic ISSN | 1879-0534 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 158 |
Article Number | 106848 |
DOI | https://doi.org/10.1016/j.compbiomed.2023.106848 |
Keywords | Delivery of Health Care, Information Dissemination, Electronic health record (EHR), Humans, Privacy, Privacy preservation, Electronic Health Records, Artificial intelligence (AI), Artificial Intelligence |
Public URL | https://uwe-repository.worktribe.com/output/10723634 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S001048252300313X?via%3Dihub |
Files
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
(2.9 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Deep learning-based multi-target regression for traffic-related air pollution forecasting
(2023)
Journal Article
SegCrop: Segmentation-based dynamic cropping of endoscopic videos to address label leakage in surgical tool detection
(2023)
Presentation / Conference
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search