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Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.

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Encrypted Traffic Analysis

Encrypted Traffic Analysis

This report explores the current state of affairs in Encrypted Traffic Analysis and in particular discusses research and methods in 6 key use cases; viz. application identification, network analytics, user information identification, detection of encrypted malware, file/device/website/location fingerprinting and DNS tunnelling detection.

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SciPy Programming Succinctly

SciPy Programming Succinctly

The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming.

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Data Structures Succinctly Part 2

Data Structures Succinctly Part 2

Data Structures Succinctly Part 2 is your concise guide to skip lists, hash tables, heaps, priority queues, AVL trees, and B-trees. As with the first book, you’ll learn how the structures behave, how to interact with them, and their performance limitations. Starting with skip lists and hash tables, and then moving to complex AVL trees and B-trees, author Robert Horvick explains what each structure’s methods and classes are, the algorithms behind them, and what is necessary to keep them valid.

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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

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