Privacy-preserving artificial intelligence in healthcare: Techniques and applications

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

【Source】COMPUTERS IN BIOLOGY AND MEDICINE

【影响因子】6.698

【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.

【Keywords】Privacy; Privacy preservation; Electronic health record (EHR); Artificial intelligence (AI)

【发表时间】2023 MAY

【收录时间】2023-05-19

【文献类型】观点阐述

【主题类别】

区块链应用-实体经济-医疗领域