Elizabeth Shinn
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Researcher
Graduate Student Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Elizabeth Shinn's research focuses on the comparative evaluation of artificial intelligence (AI)-based tools in medical diagnostics. Her recent publication investigates an AI-based scanner-agnostic add-on utility against conventional karyotyping software for diagnostic purposes. Shinn collaborates with researchers at the University of Arkansas for Medical Sciences, including Janet L. Lukacs, Marian Johnson, Misty Koch, and Cody Felty, with whom she has co-authored publications. Her work is situated within the field of medical imaging and diagnostic technologies, exploring the application of machine learning in healthcare.
Metrics
- Publications: 1
Selected Publications
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P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software (2026)
Collaboration Network
Top Collaborators
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
- P664: Comparative evaluation of an AI-based scanner-agnostic add-on utility versus conventional karyotyping software
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