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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Radiology, 2019-06, Vol.291 (3), p.781-791

2019

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Artificial intelligence for precision education in radiology

British journal of radiology, 2019-11, Vol.92 (1103), p.20190389-20190389

2019

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Patients' Use and Evaluation of an Online System to Annotate Radiology Reports with Lay Language Definitions

Academic radiology, 2017-09, Vol.24 (9), p.1169-1174

2017

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Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

Insights into imaging, 2019-10, Vol.10 (1), p.1-6, Article 101

2019

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Editorial Comment: Real-World Imaging Artificial Intelligence Considerations for COVID-19 and Beyond

American journal of roentgenology (1976), 2022-07, Vol.219 (1), p.23-23

Cook, Tessa S.

2022

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Improving Abnormality Detection on Chest Radiography Using Game-Like Reinforcement Mechanics

Academic radiology, 2017-11, Vol.24 (11), p.1428-1435

2017

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Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care

Journal of the American Medical Informatics Association : JAMIA, 2024-05, Vol.31 (6), p.1436-1440

2024

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Business Intelligence for the Radiologist: Making Your Data Work for You

Journal of the American College of Radiology, 2014-12, Vol.11 (12), p.1238-1240

2014

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The Importance of Imaging Informatics and Informaticists in the Implementation of AI

Academic radiology, 2020-01, Vol.27 (1), p.113-116

Cook, Tessa S.

2020

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Attending Radiologist Variability and Its Effect on Radiology Resident Discrepancy Rates

Academic radiology, 2017-06, Vol.24 (6), p.694-699

2017

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Capricorn–A Web-Based Automatic Case Log and Volume Analytics for Diagnostic Radiology Residents

Academic radiology, 2015-10, Vol.22 (10), p.1242-1251

2015

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Basic Artificial Intelligence Techniques: Natural Language Processing of Radiology Reports

The Radiologic clinics of North America, 2021-11, Vol.59 (6), p.919-931

2021

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Artificial Intelligence Governance and Strategic Planning: How We Do It

Journal of the American College of Radiology, 2023-09, Vol.20 (9), p.825-827

2023

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Implementation of an Automated Radiology Recommendation-Tracking Engine for Abdominal Imaging Findings of Possible Cancer

Journal of the American College of Radiology, 2017-05, Vol.14 (5), p.629-636

2017

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Why Isn't There More High-fidelity Simulation Training in Diagnostic Radiology? Results of a Survey of Academic Radiologists

Academic radiology, 2016-07, Vol.23 (7), p.870-876

2016

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Patient- and Family-Centered Care in Radiology: Lessons Learned and Next Steps

Journal of the American College of Radiology, 2024-01, Vol.21 (1), p.5-6

2024

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Artificial Intelligence Has Its Foot on the Gas Pedal–What It Means for Imaging

Journal of the American College of Radiology, 2023-09, Vol.20 (9), p.823-824

2023

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Point Set Registration Using Havrda-Charvat-Tsallis Entropy Measures

IEEE transactions on medical imaging, 2011-02, Vol.30 (2), p.451-460

2011

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Detecting Common Sources of AI Bias: Questions to Ask When Procuring an AI Solution

Radiology, 2023-05, Vol.307 (3), p.e230580-e230580

2023

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Patient-centered Radiology: Overview and Opportunities in Cardiothoracic Imaging

Journal of thoracic imaging, 2020-03, Vol.35 (2), p.79-84

Cook, Tessa S

2020

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Large datasets, logistics, sharing and workflow in screening

British journal of radiology, 2018-10, Vol.91 (1090), p.20170751-20170751

Cook, Tessa S

2018

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Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

Radiology, 2019-11, Vol.293 (2), p.436-440

2019

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Out of the Darkness and Into the Light: Patients, Referring Physicians, and Radiologists Working Toward Patient- and Family-Centered Care in Radiology

Journal of the American College of Radiology, 2017-04, Vol.14 (4), p.569-572

2017

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Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods

Journal of the American Medical Informatics Association : JAMIA, 2023-01, Vol.30 (2), p.318-328

2023

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Artificial Intelligence and the Trainee Experience in Radiology

Journal of the American College of Radiology, 2020-11, Vol.17 (11), p.1388-1393

2020

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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Artificial intelligence for precision education in radiology

Patients' Use and Evaluation of an Online System to Annotate Radiology Reports with Lay Language Definitions

Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

Editorial Comment: Real-World Imaging Artificial Intelligence Considerations for COVID-19 and Beyond

Improving Abnormality Detection on Chest Radiography Using Game-Like Reinforcement Mechanics

Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care

Business Intelligence for the Radiologist: Making Your Data Work for You

The Importance of Imaging Informatics and Informaticists in the Implementation of AI

Attending Radiologist Variability and Its Effect on Radiology Resident Discrepancy Rates

Capricorn–A Web-Based Automatic Case Log and Volume Analytics for Diagnostic Radiology Residents

Basic Artificial Intelligence Techniques: Natural Language Processing of Radiology Reports

Artificial Intelligence Governance and Strategic Planning: How We Do It

Implementation of an Automated Radiology Recommendation-Tracking Engine for Abdominal Imaging Findings of Possible Cancer

Why Isn't There More High-fidelity Simulation Training in Diagnostic Radiology? Results of a Survey of Academic Radiologists

Patient- and Family-Centered Care in Radiology: Lessons Learned and Next Steps

Artificial Intelligence Has Its Foot on the Gas Pedal–What It Means for Imaging

Point Set Registration Using Havrda-Charvat-Tsallis Entropy Measures

Detecting Common Sources of AI Bias: Questions to Ask When Procuring an AI Solution

Patient-centered Radiology: Overview and Opportunities in Cardiothoracic Imaging

Large datasets, logistics, sharing and workflow in screening

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

Out of the Darkness and Into the Light: Patients, Referring Physicians, and Radiologists Working Toward Patient- and Family-Centered Care in Radiology

Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods

Artificial Intelligence and the Trainee Experience in Radiology

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