Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10043-1.pdf
Reference21 articles.
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2. Chassagnon G, Vakalopoulou M, Paragios N, Revel M-P (2020) Artificial intelligence applications for thoracic imaging. Eur J Radiol 123:108774. https://doi.org/10.1016/j.ejrad.2019.108774
3. Chassagnon G, Vakalopoulou M, Régent A et al (2020) Deep learning–based approach for automated assessment of interstitial lung disease in systemic sclerosis on CT images. Radiol Artif Intell 2:e190006. https://doi.org/10.1148/ryai.2020190006
4. Chassagnon G, Vakalopoulou M, Battistella E et al (2021) AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Med Image Anal 67:101860. https://doi.org/10.1016/j.media.2020.101860
5. Yoo H, Kim KH, Singh R et al (2020) Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open 3:e2017135. https://doi.org/10.1001/jamanetworkopen.2020.17135
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