Learning objectives
Learning Objectives
The reader will be able to:
Understand the basic concepts of artificial intelligence;
Read and talk about topics surrounding artificial intelligence using consistent and accepted definitions of basic terminology;
Obtain an improved understanding of some of the ways in which artificial intelligence will shape the future of radiology;
Be familiar with common terminology used when evaluating studies of artificial intelligence.
Background
Background
Artificial Intelligence (AI) is one of the most exciting and rapidly developing areas of research and development in medicine, and in particular Radiology. It is an area of increasing interest and it is vital for radiologists to have a thorough understanding of the topic; however AI is developing so quickly that teaching in many cases has not yet caught up.
Procedure Details
A literature search of Medline was conducted using the key terms ‘Radiology’ AND ‘Artificial Intelligence’ / ‘deep learning’ / ‘neural networks’ /...
Imaging findings OR Procedure details
Basic Concepts
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Potential Areas for AI in Imaging
[Fig 3]
Conclusion
Interpreting AI
While most doctors are familiar with metrics such as sensitivity and specificity, which are still used when reporting AI research, there are other metrics applicable to computer science (and also some overlap of terms). To understand this new universe of AI, doctors will need to acquire a new language.
AUC-ROC
Area Under Curve under Receiver Operating Characteristics curve. The curve is derived by plotting the True Positive Rate (or Sensitivity) against the False Positive Rate (or 1-Specificity)9. It is worth noting that 0.5...
References
Health Professionals & Consumers [Internet]. RANZCR. [cited 2020 Feb 29]. Available from: https://www.ranzcr.com/college/document-library/artificial-intelligence-the-state-of-play-2019
Buda M, Wildman-Tobriner B, Hoang JK, Thayer D, Tessler FN, Middleton WD, et al. Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. Radiology. 2019;292(3):695–701.
Lell MM, Kachelrieß M. Recent and Upcoming Technological Developments in Computed Tomography. Investigative Radiology. 2020;55(1):8–19.
Couteaux V, Si-Mohamed S, Renard-Penna R, Nempont O, Lefevre T, Popoff A, et al. Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation. Diagnostic and...