Semantic Scholar Open Access 2023 360 sitasi

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

N. Khalid Adnan Qayyum M. Bilal Ala I. Al-Fuqaha Junaid Qadir

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Medicine Computer Science

Penulis (5)

N. Khalid

Adnan Qayyum

M. Bilal

Ala I. Al-Fuqaha

Junaid Qadir

Format Sitasi

APA MLA BibTeX

Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A.I., Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. https://doi.org/10.1016/j.compbiomed.2023.106848

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.compbiomed.2023.106848

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 360×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.compbiomed.2023.106848
Akses: Open Access ✓