Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists

Radiology is a specialty that is closely related to technology and therefore constantly subject to change. Artificial intelligence (AI) based upon machine learning techniques is a development that will have a significant impact on the specialty. In this chapter the question is asked what radiologists can expect from this in the short and long term. Several strategies for development, adaptation, and implementation of AI in radiological practice are presented. The remaining challenges and risks of using AI-based applications are explained, and the most relevant ethical issues are addressed.

[1]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[2]  Ronald M. Summers,et al.  Medical Image Data and Datasets in the Era of Machine Learning—Whitepaper from the 2016 C-MIMI Meeting Dataset Session , 2017, Journal of Digital Imaging.

[3]  R. Schier.  Artificial Intelligence and the Practice of Radiology: An Alternative View. , 2018, Journal of the American College of Radiology : JACR.

[4]  Klaus Fassbender,et al.  e-ASPECTS software is non-inferior to neuroradiologists in applying the ASPECT score to computed tomography scans of acute ischemic stroke patients , 2017, International journal of stroke : official journal of the International Stroke Society.

[5]  Jeroen van den Hoven,et al.  Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm , 2018, Front. Genet..

[6]  Oleg S. Pianykh,et al.  Current Applications and Future Impact of Machine Learning in Radiology. , 2018, Radiology.

[7]  Max A. Viergever,et al.  Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities , 2016, MICCAI.

[8]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[9]  E. R. Ranschaert.  Artificial Intelligence in Radiology: Hype or Hope? , 2018 .

[10]  E. Topol,et al.  Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. , 2016, JAMA.

[11]  Andrew L. Beam,et al.  Adversarial Attacks Against Medical Deep Learning Systems , 2018, ArXiv.

[12]  Andreas Holzinger,et al.  Interactive machine learning for health informatics: when do we need the human-in-the-loop? , 2016, Brain Informatics.

[13]  N. Shah,et al.  Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.

[14]  Bibb Allen,et al.  The Artificial Intelligence Ecosystem for the Radiological Sciences: Ideas to Clinical Practice. , 2018, Journal of the American College of Radiology : JACR.

[15]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[16]  Haider M. Al-Khateeb,et al.  Blockchain and Healthcare , 2019, Blockchain and Clinical Trial.

[17]  Nabile M. Safdar,et al.  Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics. , 2018, Journal of the American College of Radiology : JACR.

[18]  Fernando Collado-Mesa,et al.  The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program. , 2018, Journal of the American College of Radiology : JACR.

[19]  Jaron J. R. Chong,et al.  Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology , 2018, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[20]  Vishal Patel,et al.  A framework for secure and decentralized sharing of medical imaging data via blockchain consensus , 2019, Health Informatics J..

[21]  Marc Kohli,et al.  Ethics, Artificial Intelligence, and Radiology. , 2018, Journal of the American College of Radiology : JACR.

[22]  Bradley J. Erickson,et al.  Deep Learning in Radiology: Does One Size Fit All? , 2018, Journal of the American College of Radiology : JACR.

[23]  Turning the Tables - The New European General Data Protection Regulation. , 2018, The New England journal of medicine.

[24]  Omir Antunes Paiva,et al.  O potencial impacto da inteligência artificial na radiologia , 2017 .

[25]  Bernard F King,et al.  Artificial Intelligence and Radiology: What Will the Future Hold? , 2018, Journal of the American College of Radiology : JACR.

[26]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

[27]  B. van Ginneken,et al.  Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening , 2018, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[28]  S. Röhrich,et al.  Machine learning: from radiomics to discovery and routine , 2018, Der Radiologe.

[29]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[30]  Liang Chen,et al.  Rapid Automated Quantification of Cerebral Leukoaraiosis on CT Images: A Multicenter Validation Study. , 2018, Radiology.