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The literature below has been hand-selected as recommended reading to complement the topics covered during Season 2 of ‘AI-Use Cases: Reasons to do AI with Friends‘, brought to you by ESR Connect and the European Society of Radiology.

General AI papers:

European Society of Radiology (2019) What the radiologist should know about artificial intelligence – an ESR white paper. Insights Imaging. 10(1):44. doi: 10.1186/s13244-019-0738-2.

Choy G. et al. (2018) Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 288(2):318-328. doi: 10.1148/radiol.2018171820.

Saba L. et al (2019) The present and future of deep learning in radiology Eur J Radiol. 114:14-24. doi: 10.1016/j.ejrad.2019.02.038.

Kohli M. et al. (2017) Implementing Machine Learning in Radiology Practice and Research AJR Am J Roentgenol. 208(4):754-760. doi: 10.2214/AJR.16.17224.

Kim D.W. et al (2019) Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean J Radiol. 20(3):405-410. doi: 10.3348/kjr.2019.0025.

Rajpurkar P. et al. (2017) CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.  Available at Accessed 29 January 2020.

Doshi-Velez F. et al. (2019) Evaluating Machine Learning Articles JAMA. 322(18):1777-1779. doi:10.1001/jama.2019.17304.

Breast Cancer:

Yala A. et al. (2019) A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction Radiology. 292(1):60-66. doi: 10.1148/radiol.2019182716.

McKinney S.M. et al. (2020) International evaluation of an AI system for breast cancer screening Nature. 577(7788):89-94. doi: 10.1038/s41586-019-1799-6.

Vogl W.D. et al. (2019) Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features Eur Radiol Exp. 3(1):18. doi: 10.1186/s41747-019-0096-3.

AI in Radiology starting points:

Langs G. et al. (2018) Machine Learning: from radiomics to discovery and routine. Radiologe. 58(Suppl 1):1-6. doi: 10.1007/s00117-018-0407-3.

Lung fibrosis:

Walsh S.L.F. et al. (2018) Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 6(11):837-845. doi: 10.1016/S2213-2600(18)30286-8.

Gao M. et al. (2016) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis. 6(1):1-6. doi: 10.1080/21681163.2015.1124249.

Christe A. et al. (2019) Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Invest Radiol. 54(10):627-632. doi: 10.1097/RLI.0000000000000574.

Jacob J. et al. (2017) Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures. Eur Respir J. 49(1). pii: 1601011. doi: 10.1183/13993003.01011-2016.


The Cancer Imaging Archive (2020) The Cancer Imaging Archive. Available at Accessed 4 Feb 2020

Grossberg A. et al. (2017) Data from Head and Neck Cancer CT Atlas. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2017.umz8dv6s

TCGA-OV (2019) The Cancer Imaging Archive. Available at Accessed 4 February 2020

CT COLONOGRAPHY (2020) The Cancer Imaging Archive. Available at Accessed 4 February 2020

CodaLab (2017) LiTS – Liver Tumor Segmentation Challenge. CodaLab. Available at Accessed 4 February 2020

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.