Research graph
References from Artificial Intelligence: Promises, Pitfalls, and Ethical Considerations. Local targets link to admitted publications; unresolved targets remain external evidence.
Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients
10.1016/j.cpcardiol.2022.101330 · 2022 · External reference
Machine learning in cardiovascular magnetic resonance: basic concepts and applications
2019 · External reference
The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging
10.1007/s00247-021-05218-1 · 2022 · External reference
Foundation models for generalist medical artificial intelligence
10.1038/s41586-023-05881-4 · 2023 · External reference
Using machine learning for sequence-level automated MRI protocol selection in neuroradiology
10.1093/jamia/ocx125 · 2017 · External reference
A natural language processing-based model to automate MRI brain protocol selection and prioritization
10.1016/j.acra.2016.09.013 · 2017 · External reference
Machine learning for automation of radiology protocols for quality and efficiency improvement
10.1016/j.jacr.2020.03.012 · 2020 · External reference
Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks
10.1007/s10278-018-0066-y · 2018 · External reference
Biases in electronic health record data due to processes within the healthcare system: retrospective observational study
2018 · External reference
Artificial intelligence and clinical decision support for radiologists and referring providers
10.1016/j.jacr.2019.06.010 · 2019 · External reference
Clinical language search algorithm from free-text: facilitating appropriate imaging
10.1186/s12880-022-00740-6 · 2022 · External reference
Society for cardiovascular magnetic resonance (SCMR) guidelines for reporting cardiovascular magnetic resonance examinations
10.1186/s12968-021-00827-z · 2022 · External reference
Choosing wisely: helping physicians and patients make smart decisions about their care
10.1001/jama.2012.476 · 2012 · External reference
Automatic slice alignment method for cardiac magnetic resonance imaging
10.1007/s10334-012-0361-4 · 2013 · External reference
10.1007/978-3-642-23626-6_59
10.1007/978-3-642-23626-6_59 · External reference
The applications of artificial intelligence in cardiovascular magnetic resonance—a comprehensive review
10.3390/jcm11102866 · 2022 · External reference
Unresolved reference
External reference
Unresolved reference
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Artificial intelligence for contrast-free MRI: scar assessment in myocardial infarction using deep learning–based virtual native enhancement
10.1161/circulationaha.122.060137 · 2022 · External reference
Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
10.1109/tmi.2021.3075856 · 2021 · External reference
On instabilities of deep learning in image reconstruction and the potential costs of AI
10.1073/pnas.1907377117 · 2020 · External reference
10.1101/2023.11.13.23298477
10.1101/2023.11.13.23298477 · External reference
On hallucinations in tomographic image reconstruction
10.1109/tmi.2021.3077857 · 2021 · External reference
Unresolved reference
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A survey on bias and fairness in machine learning
2021 · External reference
Unresolved reference
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Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities
10.1016/j.compbiomed.2020.104065 · 2020 · External reference
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
10.1038/s41591-021-01595-0 · 2021 · External reference
Race/ethnic differences in the associations of the framingham risk factors with carotid IMT and cardiovascular events
10.1371/journal.pone.0132321 · 2015 · External reference
Deep neural network architectures for cardiac image segmentation
2023 · External reference
Retraining convolutional neural networks for specialized cardiovascular imaging tasks: lessons from tetralogy of fallot
10.1007/s00246-020-02518-5 · 2021 · External reference
Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance
10.1016/s2589-7500(20)30267-3 · 2021 · External reference
Unresolved reference
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Clinical text summarization: adapting large language models can outperform human experts
2023 · External reference
AI-based fully automated left atrioventricular coupling index as a prognostic marker in patients undergoing stress CMR
10.1016/j.jcmg.2023.02.015 · 2023 · External reference
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study
10.1148/radiol.2016161315 · 2017 · External reference
Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy
10.1016/j.ejrad.2019.06.004 · 2019 · External reference
10.1109/isbi53787.2023.10230698
10.1109/isbi53787.2023.10230698 · External reference
Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records
10.1038/s41598-023-30657-1 · 2023 · External reference
Addressing Artificial Intelligence Bias in Retinal Diagnostics
10.1167/tvst.10.2.13 · 2021 · External reference
Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation
10.3389/fcvm.2022.859310 · 2022 · External reference
Addressing artificial intelligence bias in retinal diagnostics
10.1167/tvst.10.2.13 · 2021 · External reference
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
10.1073/pnas.1919012117 · 2020 · External reference
“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation, and mitigation
10.1016/j.jacr.2023.06.025 · 2023 · External reference
Unresolved reference
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Unresolved reference
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Addressing fairness in artificial intelligence for medical imaging
10.1038/s41467-022-32186-3 · 2022 · External reference
Unresolved reference
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Unresolved reference
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AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs
2021 · External reference
Continuous learning AI in radiology: implementation principles and early applications
10.1148/radiol.2020200038 · 2020 · External reference
Impact of different mammography systems on artificial intelligence performance in breast cancer screening
10.1148/ryai.220146 · 2023 · External reference
Unresolved reference
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A continual learning survey: defying forgetting in classification tasks
2022 · External reference
Continuous learning ai in radiology: implementation principles and early applications
10.1148/radiol.2020200038 · 2020 · External reference
Appropriate reliance on artificial intelligence in radiology education
10.1016/j.jacr.2023.04.019 · 2023 · External reference
Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance
10.1148/radiol.222176 · 2023 · External reference
The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination
10.1038/s41598-023-36435-3 · 2023 · External reference
Unresolved reference
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10.18653/v1/p19-1355
10.18653/v1/p19-1355 · External reference
Artificial intelligence in the NHS: climate and emissions
2021 · External reference
Unresolved reference
External reference
The ethics of AI in health care: a mapping review
10.1016/j.socscimed.2020.113172 · 2020 · External reference
Tackling COVID-19 through responsible AI innovation: five steps in the right direction
10.1162/99608f92.4bb9d7a7 · 2020 · External reference
Ethics of artificial intelligence in radiology: summary of the joint european and north american multisociety statement
10.1016/j.jacr.2019.07.028 · 2019 · External reference
Prospects for cardiovascular medicine using artificial intelligence
10.1016/j.jjcc.2021.10.016 · 2022 · External reference
From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices
10.1007/s11948-019-00165-5 · 2020 · External reference
Principles alone cannot guarantee ethical AI
10.1038/s42256-019-0114-4 · 2019 · External reference
Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies
10.1007/s43681-022-00178-0 · 2023 · External reference
Unresolved reference
External reference
10.2139/ssrn.4134621
10.2139/ssrn.4134621 · External reference
Unresolved reference
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Unresolved reference
External reference
10.2139/ssrn.4027875
10.2139/ssrn.4027875 · External reference
Unresolved reference
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Unresolved reference
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Unresolved reference
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Unresolved reference
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Inclusive public participation in health: Policy, practice and theoretical contributions to promote the involvement of marginalised groups in healthcare
10.1016/j.socscimed.2015.04.019 · 2015 · External reference
Unresolved reference
External reference
AI in cardiac imaging: A UK-Based perspective on addressing the ethical, social, and political challenges
10.3389/fcvm.2020.00054 · 2020 · External reference
Responsible innovation: multi-level dynamics and soft intervention practices
2013 · External reference
The UK engineering and physical sciences research council’s commitment to a framework for responsible innovation
10.1080/23299460.2014.882065 · 2014 · External reference
Responsible research and innovation: From science in society to science for society, with society
10.1093/scipol/scs093 · 2012 · External reference
A framework for responsible innovation
2013 · External reference
Public engagement with biotechnologies offers lessons for the governance of geoengineering research and beyond
10.1371/journal.pbio.1001707 · 2013 · External reference
Unresolved reference
External reference
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
10.1136/bmjopen-2020-048008 · 2021 · External reference
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
10.1136/bmjopen-2020-047709 · 2021 · External reference
Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers
10.1148/ryai.2020200029 · 2020 · External reference
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
2020 · External reference
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
10.1016/s2589-7500(20)30219-3 · 2020 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Choosing wisely: helping physicians and patients make smart decisions about their care
10.1001/jama.2012.476 · ExternalCitation · doi-reference
10.1007/978-3-642-23626-6_59
10.1007/978-3-642-23626-6_59 · ExternalCitation · doi-reference
Retraining convolutional neural networks for specialized cardiovascular imaging tasks: lessons from tetralogy of fallot
10.1007/s00246-020-02518-5 · ExternalCitation · doi-reference
The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging
10.1007/s00247-021-05218-1 · ExternalCitation · doi-reference
Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks
10.1007/s10278-018-0066-y · ExternalCitation · doi-reference
Automatic slice alignment method for cardiac magnetic resonance imaging
10.1007/s10334-012-0361-4 · ExternalCitation · doi-reference
From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices
10.1007/s11948-019-00165-5 · ExternalCitation · doi-reference
Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies
10.1007/s43681-022-00178-0 · ExternalCitation · doi-reference
A natural language processing-based model to automate MRI brain protocol selection and prioritization
10.1016/j.acra.2016.09.013 · ExternalCitation · doi-reference
Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities
10.1016/j.compbiomed.2020.104065 · ExternalCitation · doi-reference
Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients
10.1016/j.cpcardiol.2022.101330 · ExternalCitation · doi-reference
Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy
10.1016/j.ejrad.2019.06.004 · ExternalCitation · doi-reference
Artificial intelligence and clinical decision support for radiologists and referring providers
10.1016/j.jacr.2019.06.010 · ExternalCitation · doi-reference
Ethics of artificial intelligence in radiology: summary of the joint european and north american multisociety statement
10.1016/j.jacr.2019.07.028 · ExternalCitation · doi-reference
Machine learning for automation of radiology protocols for quality and efficiency improvement
10.1016/j.jacr.2020.03.012 · ExternalCitation · doi-reference
Appropriate reliance on artificial intelligence in radiology education
10.1016/j.jacr.2023.04.019 · ExternalCitation · doi-reference
“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation, and mitigation
10.1016/j.jacr.2023.06.025 · ExternalCitation · doi-reference
AI-based fully automated left atrioventricular coupling index as a prognostic marker in patients undergoing stress CMR
10.1016/j.jcmg.2023.02.015 · ExternalCitation · doi-reference
Prospects for cardiovascular medicine using artificial intelligence
10.1016/j.jjcc.2021.10.016 · ExternalCitation · doi-reference
Inclusive public participation in health: Policy, practice and theoretical contributions to promote the involvement of marginalised groups in healthcare
10.1016/j.socscimed.2015.04.019 · ExternalCitation · doi-reference
The ethics of AI in health care: a mapping review
10.1016/j.socscimed.2020.113172 · ExternalCitation · doi-reference
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
10.1016/s2589-7500(20)30219-3 · ExternalCitation · doi-reference
Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance
10.1016/s2589-7500(20)30267-3 · ExternalCitation · doi-reference
Addressing fairness in artificial intelligence for medical imaging
10.1038/s41467-022-32186-3 · ExternalCitation · doi-reference
Foundation models for generalist medical artificial intelligence
10.1038/s41586-023-05881-4 · ExternalCitation · doi-reference
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
10.1038/s41591-021-01595-0 · ExternalCitation · doi-reference
Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records
10.1038/s41598-023-30657-1 · ExternalCitation · doi-reference
The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination
10.1038/s41598-023-36435-3 · ExternalCitation · doi-reference
Principles alone cannot guarantee ethical AI
10.1038/s42256-019-0114-4 · ExternalCitation · doi-reference
On instabilities of deep learning in image reconstruction and the potential costs of AI
10.1073/pnas.1907377117 · ExternalCitation · doi-reference
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
10.1073/pnas.1919012117 · ExternalCitation · doi-reference
The UK engineering and physical sciences research council’s commitment to a framework for responsible innovation
10.1080/23299460.2014.882065 · ExternalCitation · doi-reference
Using machine learning for sequence-level automated MRI protocol selection in neuroradiology
10.1093/jamia/ocx125 · ExternalCitation · doi-reference
Responsible research and innovation: From science in society to science for society, with society
10.1093/scipol/scs093 · ExternalCitation · doi-reference
10.1101/2023.11.13.23298477
10.1101/2023.11.13.23298477 · ExternalCitation · doi-reference
10.1109/isbi53787.2023.10230698
10.1109/isbi53787.2023.10230698 · ExternalCitation · doi-reference
Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
10.1109/tmi.2021.3075856 · ExternalCitation · doi-reference
On hallucinations in tomographic image reconstruction
10.1109/tmi.2021.3077857 · ExternalCitation · doi-reference
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
10.1136/bmjopen-2020-047709 · ExternalCitation · doi-reference
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
10.1136/bmjopen-2020-048008 · ExternalCitation · doi-reference
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study
10.1148/radiol.2016161315 · ExternalCitation · doi-reference
Continuous learning ai in radiology: implementation principles and early applications
10.1148/radiol.2020200038 · ExternalCitation · doi-reference
Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance
10.1148/radiol.222176 · ExternalCitation · doi-reference
Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers
10.1148/ryai.2020200029 · ExternalCitation · doi-reference
Impact of different mammography systems on artificial intelligence performance in breast cancer screening
10.1148/ryai.220146 · ExternalCitation · doi-reference
Artificial intelligence for contrast-free MRI: scar assessment in myocardial infarction using deep learning–based virtual native enhancement
10.1161/circulationaha.122.060137 · ExternalCitation · doi-reference
Tackling COVID-19 through responsible AI innovation: five steps in the right direction
10.1162/99608f92.4bb9d7a7 · ExternalCitation · doi-reference
Addressing artificial intelligence bias in retinal diagnostics
10.1167/tvst.10.2.13 · ExternalCitation · doi-reference
Clinical language search algorithm from free-text: facilitating appropriate imaging
10.1186/s12880-022-00740-6 · ExternalCitation · doi-reference
Society for cardiovascular magnetic resonance (SCMR) guidelines for reporting cardiovascular magnetic resonance examinations
10.1186/s12968-021-00827-z · ExternalCitation · doi-reference
Public engagement with biotechnologies offers lessons for the governance of geoengineering research and beyond
10.1371/journal.pbio.1001707 · ExternalCitation · doi-reference
Race/ethnic differences in the associations of the framingham risk factors with carotid IMT and cardiovascular events
10.1371/journal.pone.0132321 · ExternalCitation · doi-reference
10.18653/v1/p19-1355
10.18653/v1/p19-1355 · ExternalCitation · doi-reference
10.2139/ssrn.4027875
10.2139/ssrn.4027875 · ExternalCitation · doi-reference
10.2139/ssrn.4134621
10.2139/ssrn.4134621 · ExternalCitation · doi-reference
AI in cardiac imaging: A UK-Based perspective on addressing the ethical, social, and political challenges
10.3389/fcvm.2020.00054 · ExternalCitation · doi-reference
Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation
10.3389/fcvm.2022.859310 · ExternalCitation · doi-reference
The applications of artificial intelligence in cardiovascular magnetic resonance—a comprehensive review
10.3390/jcm11102866 · ExternalCitation · doi-reference
