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Clearing Opacity Through Machine Learning, W. Nicholson Price Ii, Arti K. Rai
Clearing Opacity Through Machine Learning, W. Nicholson Price Ii, Arti K. Rai
Articles
Artificial intelligence and machine learning represent powerful tools in many fields, ranging from criminal justice to human biology to climate change. Part of the power of these tools arises from their ability to make predictions and glean useful information about complex real-world systems without the need to understand the workings of those systems.
Regulating Black-Box Medicine, W. Nicholson Price Ii
Regulating Black-Box Medicine, W. Nicholson Price Ii
Michigan Law Review
Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they …
Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii
Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii
Articles
Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques—terms I will explain momentarily—innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from healthcare data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including …
The Oversimplification Of Deregulation: A Case Study On Clinical Decision Support Software, Deeva V. Shah
The Oversimplification Of Deregulation: A Case Study On Clinical Decision Support Software, Deeva V. Shah
Michigan Telecommunications & Technology Law Review
Until the December 2016 passage of the Cures Act, the FDA had regulatory power over clinical decision support (CDS) software; however, the Act removed a large group of CDS software from the FDA’s statutory authority. Congressional intent was to increase innovation by removing regulatory blockades—such as device testing and certification—from the FDA’s purview. This note argues that the enactment of this specific provision of the Act will instead stymie innovation and overlook the unfortunate safety consequences inherent in its deregulation. CDS software is a burgeoning field ripe for innovation; however, rapid innovation can often lead to a slew of mistakes—mistakes …
To Innovate Or Not To Innovate, That Is The Question: The Functions, Failures, And Foibles Of The Reward Function Theory Of Patent Law In Relation To Computer Software Platforms , Seth A. Cohen
Michigan Telecommunications & Technology Law Review
The patent system has traditionally been viewed as having two primary functions: the reward function and the prospect function. Although these theories do explain some behavior which results from the practical applications of the patent system, they also overlook some behavior of the patent system which indicates a failure of these functions. In order to properly prevent such failure, this paper proposes that the patent system adopt an orientation that will lead to increased innovative rivalry and competition. In Part I, using the computer operating system software market as an example, I propose a framework for reconceptualizing patent protection as …