Journal Publication

Automatic Standard Plane and Diagnostic Usability Classification In Obstetric Ultrasounds
Objective: This study introduces an innovative end-to-end deep learning pipeline designed to automatically classify and order fetal …
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Empirical data drift detection experiments on real-world medical imaging data
While it is common to monitor deployed clinical artificial intelligence (AI) models for performance degradation, it is less common for …
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The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups
Purpose: To externally test four chest radiograph (CXR) classifiers on a large, diverse, real-world dataset with robust subgroup …
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Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels
Despite frequent reports of imaging artificial intelligence (AI) that parallels human performance, clinicians often question the safety …
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U “AI” Testing: User Interface and Usability Testing of a Chest X-ray AI Tool in a Simulated Real-World Workflow
Purpose: To observe interactions of practicing radiologists with a chest x-ray AI tool and evaluate its usability and impact on …
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How artificial intelligence can help us ‘Choose Wisely’
The overuse of low value medical tests and treatments drives costs and patient harm. Efforts to address overuse, such as Choosing …
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Machine learning for automation of radiology protocols for quality and efficiency improvement
Purpose: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, …
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An awakening in medicine: the partnership of humanity and intelligent machines
In concurrence with the introduction of the internet, widely networked computers, and the collection of large amounts of digital data, …
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