Research graph
References from Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.ejmp.2021.04.016
10.1016/j.ejmp.2021.04.016 · External reference
Applications of artificial intelligence in cardiovascular imaging
10.1038/s41569-021-00527-2 · 2021 · External reference
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10.1016/j.cpet.2021.09.007 · External reference
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10.1148/ryai.2020190043 · External reference
A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging
10.1016/j.ejrad.2023.111159 · 2023 · External reference
10.3390/diagnostics13172760
10.3390/diagnostics13172760 · External reference
A review of explainable artificial intelligence in healthcare
10.1016/j.compeleceng.2024.109370 · 2024 · External reference
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
10.1038/s41746-022-00699-2 · 2022 · External reference
Designing interpretable ML system to enhance trust in healthcare: a systematic review to proposed responsible clinician-AI-collaboration framework
10.1016/j.inffus.2024.102412 · 2024 · External reference
Interpretability of machine learning‐based prediction models in healthcare
10.1002/widm.1379 · 2020 · External reference
What do radiologists look for? Advances and limitations of perceptual learning in radiologic search
10.1167/jov.20.10.17 · 2020 · External reference
10.3758/s13414-019-01695-7
10.3758/s13414-019-01695-7 · External reference
Radiologists’ usage of diagnostic AI systems: the role of diagnostic self-efficacy for sensemaking from confirmation and disconfirmation
10.1007/s12599-022-00750-2 · 2022 · External reference
10.1038/d41586-022-00858-1
10.1038/d41586-022-00858-1 · External reference
10.1007/s13347-021-00477-0
10.1007/s13347-021-00477-0 · External reference
Exploring the artificial intelligence “Trust paradox”: evidence from a survey experiment in the United States
10.1371/journal.pone.0288109 · 2023 · External reference
Hello AI": uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making
10.1145/3359206 · 2019 · External reference
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Blinded review of retrospectively visible unreported breast cancers: an eye-position analysis
10.1148/radiol.2211001507 · 2001 · External reference
Effects of lesion conspicuity on visual search in mammogram reading1
10.1016/j.acra.2005.03.068 · 2005 · External reference
Shedding light on ai in radiology: a systematic review and taxonomy of eye gaze-driven interpretability in deep learning
10.1016/j.ejrad.2024.111341 · 2024 · External reference
Vid2seq: large-scale pretraining of a visual language model for dense video captioning. In Proceedings of
2023 · External reference
End-to-end dense video captioning with parallel decoding
2021 · External reference
Unresolved reference
2024 · External reference
Enhancing traffic safety with parallel dense video captioning for end-to-end event analysis. In Proceedings of
2024 · External reference
MPP-net: multi-perspective perception network for dense video captioning
10.1016/j.neucom.2023.126523 · 2023 · External reference
Unresolved reference
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How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications
10.3390/bioengineering10121435 · 2023 · External reference
How does artificial intelligence in radiology improve efficiency and health outcomes?
2022 · External reference
Physician perspectives on integration of artificial intelligence into diagnostic pathology
10.1038/s41746-019-0106-0 · 2019 · External reference
Unresolved reference
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Lesion location attention guided network for multi-label thoracic disease classification in chest X-rays
10.1109/jbhi.2019.2952597 · 2019 · External reference
Unresolved reference
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10.1148/ryai.2020190043
10.1148/ryai.2020190043 · External reference
10.1016/j.compbiomed.2021.105111
10.1016/j.compbiomed.2021.105111 · External reference
Interpretation and visualization techniques for deep learning models in medical imaging
10.1088/1361-6560/abcd17 · 2021 · External reference
10.1016/s2589-7500(21)00208-9
10.1016/s2589-7500(21)00208-9 · External reference
A survey on explainable artificial intelligence (XAI): towards medical AI
10.1109/tnnls.2020.3027314 · 2020 · External reference
Unresolved reference
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Grad-cam: Visual explanations from deep networks via gradient-based localization
2017 · External reference
Examples are not enough, learn to criticize! criticism for interpretability
2022 · External reference
Interpretable machine learning: Fundamental principles and 10 grand challenges
10.1214/21-ss133 · 2022 · External reference
Prototype-based interpretability in AI for medical imaging
2021 · External reference
Axiomatic attribution for deep networks
2022 · External reference
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Visualizing transformers for medical image analysis
2023 · External reference
Consistent individualized feature attribution for tree ensembles
2020 · External reference
Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods
2021 · External reference
Momentum contrast for unsupervised visual representation learning
2022 · External reference
Bayesian deep learning for uncertainty estimation in medical imaging
2021 · External reference
Multi-modal learning for AI-driven medical image analysis: a survey
2023 · External reference
Measuring the quality of explanations: the system causability scale (SCS) for assessing explainable AI
2022 · External reference
Cognitive processes as integrative component for developing expert decision-making systems: a workflow centered framework
10.1016/j.jbi.2009.07.001 · 2010 · External reference
The invisible gorilla strikes again: sustained inattentional blindness in expert observers
10.1177/0956797613479386 · 2013 · External reference
Accuracy is in the eyes of the pathologist: the visual interpretive process and diagnostic accuracy with digital whole slide images
10.1016/j.jbi.2017.01.004 · 2017 · External reference
Eye gaze guided cross-modal alignment network for radiology report generation
2024 · External reference
The effect of expertise on eye movement behaviour in medical image perception
10.1371/journal.pone.0066169 · 2013 · External reference
Shedding light on ai in radiology: a systematic review and taxonomy of eye gaze-driven interpretability in deep learning
10.1016/j.ejrad.2024.111341 · 2024 · External reference
Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations
10.1038/s41746-024-01200-x · 2024 · External reference
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
2019 · External reference
Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development
10.1038/s41597-021-00863-5 · 2021 · External reference
REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays
10.1038/s41597-022-01441-z · 2022 · External reference
Deep residual learning for image recognition
2016 · External reference
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July. Learning transferable visual models from natural language supervision
2021 · External reference
Bidirectional attentive fusion with context gating for dense video captioning
2018 · External reference
Cider: consensus-based image description evaluation
2015 · External reference
Inter-observer variability of manual contour delineation of structures in CT
10.1007/s00330-018-5695-5 · 2019 · External reference
Commonly missed findings on chest radiographs: causes and consequences
10.1016/j.chest.2022.10.039 · 2023 · External reference
