The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
Summary
Side‐channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side‐channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side‐channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short‐term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45‐nm library.
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