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AI in Healthcare: Bridging the Gap Between Diagnostics and Data Privacy
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2023
Jahr
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 develop data-sharing methods in the age of AI that preserve patient privacy while facilitating AI-based healthcare applications. This study summarizes state-of-the-art approaches for privacy preservation in AI-driven healthcare applications, highlighting prominent techniques such as Federated Learning and Hybrid Methods. Additionally, it explores privacy attacks, security challenges, and future directions to enable responsible AI adoption.
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