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Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Radiology, 2022-12, Vol.305 (3), p.555-563

2022

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FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Academic radiology, 2022-04, Vol.29 (4), p.559-566

2022

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Federated learning for predicting clinical outcomes in patients with COVID-19

Nature medicine, 2021-10, Vol.27 (10), p.1735-1743

2021

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Validation pipeline for machine learning algorithm assessment for multiple vendors

PloS one, 2022-04, Vol.17 (4), p.e0267213-e0267213

2022

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Use of Artificial Intelligence in Clinical Neurology

Seminars in neurology, 2022-02, Vol.42 (1), p.39-47

2022

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Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

Scientific reports, 2021-01, Vol.11 (1), p.858-858, Article 858

2021

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Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Journal of the American College of Radiology, 2019-09, Vol.16 (9), p.1351-1356

2019

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No code machine learning: validating the approach on use-case for classifying clavicle fractures

Clinical imaging, 2024-08, Vol.112, p.110207, Article 110207

2024

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Artificial Intelligence Enabling Radiology Reporting

The Radiologic clinics of North America, 2021-11, Vol.59 (6), p.1045-1052

2021

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Lessons Learned From the Front Lines of Artificial Intelligence Implementation

Journal of the American College of Radiology, 2021-11, Vol.18 (11), p.1474-1475

2021

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Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT

Radiology, 2023-02, Vol.306 (2), p.e220101

2023

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Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Abdominal radiology (New York), 2021-05, Vol.46 (5), p.2097-2106

2021

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Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head

American journal of neuroradiology : AJNR, 2024-08

2024

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Head CT deep learning model is highly accurate for early infarct estimation

Scientific reports, 2023-01, Vol.13 (1), p.189-189, Article 189

2023

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Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT

Japanese journal of radiology, 2023-02, Vol.41 (2), p.194-200

2023

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Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Scientific reports, 2022-02, Vol.12 (1), p.2154-2154, Article 2154

2022

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Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas

Academic radiology, 2022-02, Vol.29 (2), p.236-244

2022

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Data Management in Artificial Intelligence–Assisted Radiology Reporting

Journal of the American College of Radiology, 2021-11, Vol.18 (11), p.1485-1488

2021

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Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

Journal of the American College of Radiology, 2023-03, Vol.20 (3), p.352-360

2023

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Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Radiology, 2022-10, Vol.305 (1), p.E62-E62

2022

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Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19

Medicine (Baltimore), 2022-07, Vol.101 (29), p.e29587-e29587

2022

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Predictive values of AI-based triage model in suboptimal CT pulmonary angiography

Clinical imaging, 2022-06, Vol.86, p.25-30

2022

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Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia

Clinical imaging, 2023-03, Vol.95, p.47-51

2023

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Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs

Academic radiology, 2023-12, Vol.30 (12), p.2921-2930

2023

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Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

JAMA network open, 2022-08, Vol.5 (8), p.e2229289-e2229289

2022

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Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Federated learning for predicting clinical outcomes in patients with COVID-19

Validation pipeline for machine learning algorithm assessment for multiple vendors

Use of Artificial Intelligence in Clinical Neurology

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

No code machine learning: validating the approach on use-case for classifying clavicle fractures

Artificial Intelligence Enabling Radiology Reporting

Lessons Learned From the Front Lines of Artificial Intelligence Implementation

Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head

Head CT deep learning model is highly accurate for early infarct estimation

Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas

Data Management in Artificial Intelligence–Assisted Radiology Reporting

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19

Predictive values of AI-based triage model in suboptimal CT pulmonary angiography

Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia

Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs

Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

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