Designing for Physician Trust: Toward a Machine Learning Decision Aid for Radiation Toxicity Risk

Volume: 28, Issue: 3, Pages: 27 - 35
Published: Dec 29, 2019
Abstract
The application of machine learning (ML) technologies in health care is expected to improve care delivery and patient outcomes. However, there are no best practices for designing these technologies for use in clinical settings. To explore user needs and design requirements for a user interface of a ML risk prediction tool in development, we consulted with subject matter experts and physicians. We explored physician expectations of using a ML...
Paper Details
Title
Designing for Physician Trust: Toward a Machine Learning Decision Aid for Radiation Toxicity Risk
Published Date
Dec 29, 2019
Journal
Volume
28
Issue
3
Pages
27 - 35
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.