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AI in Radiology

Artificial intelligence (AI) has been defined by some as the "branch of computer science dealing with the simulation of intelligent behavior in computers" 1, however, the precise definition is actually a matter of debate among experts. An alternative definition is the branch of computer science dedicated to creating algorithms that can solve problems without being explicitly programmed for all the specificities of the problems. AI algorithms and in particular deep learning (part of machine learning) aim to either assist humans with solving a problem or solve the problem without human input. The exponential increase in computational processing and memory capability has opened up the potential for AI to handle much larger datasets, including those required in radiology.

The term AI encompasses numerous specific areas and approaches, including: 


Talk of artificial intelligence (AI) has been running rampant in radiology circles. Sometimes referred to as machine learning or deep learning, AI, many believe, can and will optimize radiologists' workflows, facilitate quantitative radiology, and assist in discovering genomic markers.

Ethical Issues:

The rapid advancement of AI in medical imaging has identified a number of opportunities and challenges for maturing the field towards building robust and reliable infrastructure.

  • issues of data governance
    • data ownership
    • data sharing and exchange
    • data privacy
    • data bias
    • data quality and establishing ground truth
  • issues of algorithms
    • transparency
    • algorithm bias
  • issues of AI in radiology practice
    • liability


Of late, artificial intelligence has become the buzz word in radiology. It is hard to think of a single term that has led to such serious discussions and debates in our specialty in recent times. Lot of new technology jargon we have not been accustomed to read, let alone understand, are all over the papers, and words like convoluted neural networks (CNN), natural language processing, deep convolutional neural networks (DCNN) have become commonplace. There have been recent talks and articles stating that these algorithms will generate “Heat maps” of the areas for the radiologist to focus on or in other words using the “eyes of the software” to interpret the images.

AI can be used as an optimizing tool to assist the technologist and radiologist in choosing a personalized patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data.