Improving reference standards for validation of AI-based radiography.

Journal: The British journal of radiology

Published Date: Jul 1, 2021

Abstract

OBJECTIVE: Demonstrate the importance of combining multiple readers' opinions, in a context-aware manner, when establishing the reference standard for validation of artificial intelligence (AI) applications for, chest radiographs. By comparing individual readers, majority vote of a panel, and panel-based discussion, we identify methods which maximize interobserver agreement and label reproducibility.

Authors

Gavin E Duggan

Google Health (G.E.D., Y.L., D.T., S.S.), Stanford Health Care and Palo Alto Veterans Affairs (J.J.R.), California, California, USA.
Joshua J Reicher

Stanford Health Care and Palo Alto Veterans Affairs, Palo Alto, CA, USA.
Yun Liu

Google Health, Palo Alto, CA USA.
Daniel Tse

Google AI, Mountain View, CA, USA. tsed@google.com.
Shravya Shetty

Google AI, Mountain View, CA, USA.

Keywords

Artificial Intelligence Humans Observer Variation Quality Improvement Radiography, Thoracic Radiologists Reference Standards Reproducibility of Results

External Resources

View on PubMed Access via DOI PubMed (34142868)

Improving reference standards for validation of AI-based radiography.

Abstract

Authors

Keywords

External Resources

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