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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

BMJ (Online), 2020-03, Vol.368, p.m689

2020

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Nature medicine, 2020-09, Vol.26 (9), p.1364-1374

2020

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Nature medicine, 2020-09, Vol.26 (9), p.1351-1363

2020

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Artificial intelligence in diagnostic imaging: impact on the radiography profession

British journal of radiology, 2020-04, Vol.93 (1108), p.20190840-20190840

2020

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

The Lancet. Digital health, 2020-10, Vol.2 (10), p.e537-e548

2020

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The Lancet. Digital health, 2020-10, Vol.2 (10), p.e549-e560

2020

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Can Apple and Google continue as health app gatekeepers as well as distributors and developers?

NPJ digital medicine, 2023-01, Vol.6 (1), p.8-8, Article 8

2023

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How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products

Insights into imaging, 2024-02, Vol.15 (1), p.51-51, Article 51

2024

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Is the 510(k) Pathway Robust Enough for the Era of AI?

Applied radiology (1976), 2023-03, Vol.52 (2), p.26-27

Harvey, Hugh

2023

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Best practices for authors of healthcare-related artificial intelligence manuscripts

NPJ digital medicine, 2020-10, Vol.3 (1), p.134-134, Article 134

2020

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Large language model AI chatbots require approval as medical devices

Nature medicine, 2023-10, Vol.29 (10), p.2396-2398

2023

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The emperor has few clothes: a realistic appraisal of current AI in radiology

European radiology, 2024-03

2024

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Preparing Medical Imaging Data for Machine Learning

Radiology, 2020-04, Vol.295 (1), p.4-15

2020

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More than meets the AI: refining image acquisition and resolution

The Lancet (British edition), 2020-11, Vol.396 (10261), p.1479-1479

2020

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ (Online), 2020-09, Vol.370, p.m3164

2020

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

BMJ (Online), 2020-09, Vol.370, p.m3210

2020

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Guidance for Interventional Trials Involving Artificial Intelligence

Radiology. Artificial intelligence, 2020-11, Vol.2 (6), p.e200228-e200228

2020

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Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

Journal of the American College of Radiology, 2021-03, Vol.18 (3), p.413-424

2021

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Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK

British journal of radiology, 2023-12, Vol.96 (1152), p.20221157-20221157

2023

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Quantitative analysis of approaches to group marking

Assessment and evaluation in higher education, 2019-11, Vol.44 (8), p.1133-1147

2019

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Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK

International journal of medical informatics (Shannon, Ireland), 2024-06, Vol.186, p.105423-105423, Article 105423

2024

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Re: machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. A reply

Clinical radiology, 2019-02, Vol.74 (2), p.162-162

2019

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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Artificial intelligence in diagnostic imaging: impact on the radiography profession

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Can Apple and Google continue as health app gatekeepers as well as distributors and developers?

How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products

Is the 510(k) Pathway Robust Enough for the Era of AI?

Best practices for authors of healthcare-related artificial intelligence manuscripts

Protocol for the development of the Chatbot Assessment Reporting Tool (CHART) for clinical advice

Large language model AI chatbots require approval as medical devices

The emperor has few clothes: a realistic appraisal of current AI in radiology

Preparing Medical Imaging Data for Machine Learning

More than meets the AI: refining image acquisition and resolution

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

Guidance for Interventional Trials Involving Artificial Intelligence

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Quantitative analysis of approaches to group marking

Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK

Re: machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. A reply

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

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