Privacy-preserving artificial intelligence in healthcare: Techniques and applications

Khalid, Nazish; Qayyum, Adnan; Bilal, Muhammad; Al-Fuqaha, Ala; Qadir, Junaid

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.

• 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
• 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