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01-01-2019 | Computer Applications

Artificial intelligence in radiology: how will we be affected?

Authors: S. H. Wong, H. Al-Hasani, Z. Alam, A. Alam

Published in: European Radiology | Issue 1/2019

Excerpt

Artificial intelligence (AI) has come to the forefront of conversation amongst radiologists. Popular culture has often portrayed the far-fetched perils of AI e.g. sentient machines seeking human domination. Whilst absurd, there is an element of truth in that AI has the potential to revolutionise the way we work in the twenty-first century. …

Merriam-Webster (2018) Artificial intelligence. https://www.merriam-webster.com/dictionary/artificial%20intelligence. Accessed 24 June 2018

Machine Learning Mastery (2018) Frequently asked questions. https://machinelearningmastery.com/faq/. Accessed 24 June 2018

BBC (2017) Google AI defeats human Go champion. http://www.bbc.co.uk/news/technology-40042581. Accessed 3 Feb 2018

BBC (2016) Google's DeepMind to peek at NHS eye scans for disease analysis. http://www.bbc.co.uk/news/technology-36713308. Accessed 3 Feb 2018

The Guardian (2016) Google DeepMind and UCLH collaborate on AI-based radiotherapy treatment. https://www.theguardian.com/technology/2016/aug/30/google-deepmind-ucl-ai-radiotherapy-treatment-. Accessed 3 Feb 2018

HIT Consultant (2017) Samsung integrates with MedyMatch’s A.I. platform to improve stroke assessment in ambulances. http://hitconsultant.net/2017/03/29/samsung-integrates-medymatch/. Accessed 3 Feb 2018

Shiraishi J, Li Q, Appelbaum D, Doi K (2011) Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med 41:449–462CrossRef

Human Connectome Project (2018) http://www.humanconnectomeproject.org/. Accessed 3 Feb 2018

National Institutes of Health (2018) The BRAIN Initiative. https://braininitiative.nih.gov/. Accessed 3 Feb 2018

10.

University of California San Francisco (2016) Project to apply artificial intelligence to imaging of brain emergencies receives $1.2M. https://www.ucsf.edu/news/2016/11/405021/project-apply-artificial-intelligence-imaging-brain-emergencies-receives-12m#. Accessed 3 Feb 2018

11.

The Guardian (2017) Elon Musk: regulate AI to combat 'existential threat' before it's too late. https://www.theguardian.com/technology/2017/jul/17/elon-musk-regulation-ai-combat-existential-threat-tesla-spacex-ceo. Accessed 3 Feb 2018

12.

Department for Digital, Culture, Media & Sport and Department for Business, Energy & Industrial Strategy (2017) Growing the artificial intelligence industry in the UK. https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk. Accessed 3 Feb 2018

13.

The Royal College of Radiologists (2016) Exploring how artificial intelligence may support human interpretation of medical images. https://www.rcr.ac.uk/posts/exploring-how-artificial-intelligence-may-support-human-interpretation-medical-images. Accessed 3 Feb 2018

14.

The Royal College of Radiologists. Radiology Informatics Committee (2018) https://www.rcr.ac.uk/clinical-radiology/faculty-structure/radiology-informatics-committee. Accessed 3 Feb 2018

15.

Parliament UK (2018) Select Committee on Artificial Intelligence – timeline. http://www.parliament.uk/business/committees/committees-a-z/lords-select/ai-committee/timeline/. Accessed 3 Feb 2018

16.

BBC (2017) Google NHS deal rebuked again by DeepMind panel. http://www.bbc.co.uk/news/technology-40497020. Accessed 3 Feb 2018

17.

Ng A (2017) http://www.andrewng.org. Accessed 3 Feb 2018

18.

Chockley K, Emanuel E (2016) The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol 13:1415–1420CrossRef

19.

Obermeyer Z, Emanuel EJ (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375: 1216

20.

Collado-Mesa F, Alvarez E, Arheart K (2018) The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol. https://doi.org/10.1016/j.jacr.2017.12.021

Title: Artificial intelligence in radiology: how will we be affected?
Authors: S. H. Wong
H. Al-Hasani
Z. Alam
A. Alam
Publication date: 01-01-2019
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 1/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5644-3

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Artificial intelligence in radiology: how will we be affected?

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