European Radiology

, Volume 29, Issue 3, pp 1616–1624 | Cite as

Demystification of AI-driven medical image interpretation: past, present and future

  • Peter Savadjiev
  • Jaron Chong
  • Anthony Dohan
  • Maria Vakalopoulou
  • Caroline Reinhold
  • Nikos Paragios
  • Benoit GallixEmail author
Computer Applications


The recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it.

Key Points

• Artificial intelligence (AI) research in medical imaging has a long history

• The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods.

• A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.


Diagnostic imaging Artificial intelligence (AI) Machine learning Computer-assisted image processing Computer-assisted image interpretation 



Artificial intelligence


Artificial neural network


Least absolute shrinkage and selection operator


Patient/problem, intervention, comparison intervention and outcomes





The authors state that this work has not received any funding.

Compliance with Ethical Standards


The scientific guarantor of this publication is Dr. Benoit Gallix.

Conflict of Interest

Professor Nikos Paragions declares a relationship with the following company: TheraPanacea, Paris, France.

The other co-authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and Biometry

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was not required for this study because this is a review article, no study was performed.

Ethical Approval

Institutional review board approval was not required because this is a review article and no study was performed.


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Peter Savadjiev
    • 1
  • Jaron Chong
    • 1
    • 2
  • Anthony Dohan
    • 1
    • 2
    • 3
  • Maria Vakalopoulou
    • 4
    • 5
  • Caroline Reinhold
    • 1
    • 2
  • Nikos Paragios
    • 4
    • 6
  • Benoit Gallix
    • 1
    • 2
    Email author
  1. 1.Department of Diagnostic RadiologyMcGill UniversityMontrealCanada
  2. 2.Department of Diagnostic RadiologyMcGill University Health CentreMontrealCanada
  3. 3.Department of Body & Interventional Imaging, Hôpital Lariboisière-AP-HPUniversité Diderot-Paris 7 and INSERM U965Paris Cedex 10France
  4. 4.Ecole CentraleSupelecGif-sur-YvetteFrance
  5. 5.Inria Saclay/Ile-de-FrancePalaiseauFrance
  6. 6.TheraPanaceaParisFrance

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