Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Law
Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs
Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs
Articles
Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …
Distributed Governance Of Medical Ai, W. Nicholson Price Ii
Distributed Governance Of Medical Ai, W. Nicholson Price Ii
Law & Economics Working Papers
Artificial intelligence (AI) promises to bring substantial benefits to medicine. In addition to pushing the frontiers of what is humanly possible, like predicting kidney failure or sepsis before any human can notice, it can democratize expertise beyond the circle of highly specialized practitioners, like letting generalists diagnose diabetic degeneration of the retina. But AI doesn’t always work, and it doesn’t always work for everyone, and it doesn’t always work in every context. AI is likely to behave differently in well-resourced hospitals where it is developed than in poorly resourced frontline health environments where it might well make the biggest difference …
Cbct In Clinical Practice, Tarunjeet Pabla Bds, Dmd, Ms, Dip. Abomr, Hugo C. Campos Dds, Dmd, Mds, Dip. Abomr, Aruna Ramesh Bds, Dmd, Ms, Dip. Abomr
Cbct In Clinical Practice, Tarunjeet Pabla Bds, Dmd, Ms, Dip. Abomr, Hugo C. Campos Dds, Dmd, Mds, Dip. Abomr, Aruna Ramesh Bds, Dmd, Ms, Dip. Abomr
The Journal of the Michigan Dental Association
This feature explores the integration of Cone Beam Computed Tomography (CBCT) into dental practice, offering guidelines for best practices. Introduced in 2001, CBCT revolutionized dental radiography, impacting various clinical areas. The article emphasizes the need for clinicians to comprehend CBCT technology, its benefits, and potential risks. It delves into CBCT imaging considerations, technical parameters (Field of View, Voxel Size, Spatial and Contrast Resolution), image viewing, artifacts, machine calibration, and service. Addressing radiation dose, risks, and protection, the article outlines decision-making for 2D vs. 3D imaging. It underscores the responsibility of interpreting CBCT images, legal considerations, return on investment, and the …
New Innovation Models In Medical Ai, W Nicholson Price Ii, Rachel E. Sachs, Rebecca S. Eisenberg
New Innovation Models In Medical Ai, W Nicholson Price Ii, Rachel E. Sachs, Rebecca S. Eisenberg
Articles
In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those that resemble traditional medical devices in their functions—have received substantial attention in the legal and policy literature. But other types of novel AI technologies, such as those related to quality improvement and optimizing use of scarce facilities, have been largely absent from the discussion thus far. These AI innovations have the potential to shed light on important aspects of health innovation policy. First, these AI innovations interact less with the legal regimes that scholars traditionally conceive of as …
Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen
Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen
Law & Economics Working Papers
While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers.
Artificial Intelligence In Canadian Healthcare: Will The Law Protect Us From Algorithmic Bias Resulting In Discrimination?, Bradley Henderson, Colleen M. Flood, Teresa Scassa
Artificial Intelligence In Canadian Healthcare: Will The Law Protect Us From Algorithmic Bias Resulting In Discrimination?, Bradley Henderson, Colleen M. Flood, Teresa Scassa
Canadian Journal of Law and Technology
In this article, we canvas why AI may perpetuate or exacerbate extant discrimination through a review of the training, development, and implementation of healthcare-related AI applications and set out policy options to militate against such discrimination. The article is divided into eight short parts including this introduction. Part II focuses on explaining AI, some of its basic functions and processes, and its relevance to healthcare. In Part III, we define and explain the difference and relationship between algorithmic bias and data bias, both of which can result in discrimination in healthcare settings, and provide some prominent examples of healthcare-related AI …