期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 158, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2023.106848
关键词
Privacy; Privacy preservation; Electronic health record (EHR); Artificial intelligence (AI)
There is a growing interest in translating AI research into clinically-validated applications for healthcare services. However, the widespread adoption of AI-based applications faces barriers such as non-standardized medical records, limited availability of datasets, and privacy concerns. This study provides a summary of state-of-the-art privacy-preserving techniques for AI-based healthcare applications.
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.
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