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Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i.e., they only learn features from pixel-level information.
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Imon Banerjee, Matthew C Chen, Matthew P Lungren, and Daniel L Rubin · 2018
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Andrew L Beam and Isaac S Kohane · 2018
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A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
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Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Jeremy Kawahara, Sara Daneshvar, Giuseppe Argenziano, and Ghassan Hamarneh · 2018
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Scalable and accurate deep learning with electronic health records
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Comparative effectiveness of convolutional neural network (cnn) and recurrent neural network (rnn) architectures for radiology text report classification
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Development and performance of the pulmonary embolism result forecast model (perform) for computed tomography clinical decision support
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Ethical machine learning in healthcare
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Penet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric ct imaging
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Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection
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The uk biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions
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Chexclusion: Fairness gaps in deep chest x-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2020
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Reading race: Ai recognises patient’s racial identity in medical images
Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, et al · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition
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Achieving fairness in medical devices
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Estimating and improving fairness with adversarial learning
Xiaoxiao Li, Ziteng Cui, Yifan Wu, Lin Gu, and Tatsuya Harada · 2021
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An empirical characterization of fair machine learning for clinical risk prediction
Stephen R Pfohl, Agata Foryciarz, and Nigam H Shah · 2021
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