Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

Eur Radiol. 2020 Jun;30(6):3576-3584. doi: 10.1007/s00330-020-06672-5. Epub 2020 Feb 17.


Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. KEY POINTS: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.

Keywords: Artificial intelligence; Bioethics; Data; Education; Regulation.

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Deep Learning
  • Forecasting
  • Humans
  • Information Dissemination
  • Machine Learning
  • Radiologists
  • Radiology*
  • Reproducibility of Results
  • Validation Studies as Topic