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Medical Ai And Contextual Bias, W. Nicholson Price Ii
Medical Ai And Contextual Bias, W. Nicholson Price Ii
Articles
Artificial intelligence will transform medicine. One particularly attractive possibility is the democratization of medical expertise. If black-box medical algorithms can be trained to match the performance of high-level human experts — to identify malignancies as well as trained radiologists, to diagnose diabetic retinopathy as well as board-certified ophthalmologists, or to recommend tumor-specific courses of treatment as well as top-ranked oncologists — then those algorithms could be deployed in medical settings where human experts are not available, and patients could benefit. But there is a problem with this vision. Privacy law, malpractice, insurance reimbursement, and FDA approval standards all encourage developers …
Artificial Intelligence In The Medical System: Four Roles For Potential Transformation, W. Nicholson Price Ii
Artificial Intelligence In The Medical System: Four Roles For Potential Transformation, W. Nicholson Price Ii
Articles
Artificial intelligence (AI) looks to transform the practice of medicine. As academics and policymakers alike turn to legal questions, including how to ensure high-quality performance by medical AI, a threshold issue involves what role AI will play in the larger medical system. This Article argues that AI can play at least four distinct roles in the medical system, each potentially transformative: pushing the frontiers of medical knowledge to increase the limits of medical performance, democratizing medical expertise by making specialist skills more available to non-specialists, automating drudgery within the medical system, and allocating scarce medical resources. Each role raises its …
Risk And Resilience In Health Data Infrastructure, W. Nicholson Price Ii
Risk And Resilience In Health Data Infrastructure, W. Nicholson Price Ii
Articles
Today’s health system runs on data. However, for a system that generates and requires so much data, the health care system is surprisingly bad at maintaining, connecting, and using those data. In the easy cases of coordinated care and stationary patients, the system works—sometimes. But when care is fragmented, fragmented data often result. Fragmented data create risks both to individual patients and to the system. For patients, fragmentation creates risks in care based on incomplete or incorrect information, and may also lead to privacy risks from a patched together system. For the system, data fragmentation hinders efforts to improve efficiency …
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 …