Skip to main content

Artificial Intelligence in Hybrid Imaging

Artificial intelligence (AI) refers to a field of computer science dedicated to the creation of systems performing tasks that usually require human intelligence, such as learning and problem solving. Similar to our natural intelligence, AI algorithms can look at medical images to identify patterns after being trained using vast numbers of examinations and images. Applying artificial intelligence (AI) to hybrid and multimodality medical imaging data might further leverage the use of these imaging techniques by speeding-up and increasing the diagnostic accuracy in various diseases, and supporting prediction of response on therapy.
We are at the beginning of the AI era, and it will surely impact nuclear medicine and radiology. Still, the majority of the physicians are not experienced in AI applications, due to a gap in knowledge, lack of training facilities or some among us with disbelieve in, or even fear AI. 
The aim of this thematic series is to provide a stage to describe the background, value and application of AI in medical imaging, in particular in the field of hybrid and multimodality imaging. The series intends to provide a trigger for technologist and physicians to acknowledge the scientific potential of AI in medical imaging.

Guest Editor: Prof. Dr. R. Slart

  1. Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clin...

    Authors: Lu Zhang, Jianqing Sun, Beibei Jiang, Lingyun Wang, Yaping Zhang and Xueqian Xie
    Citation: European Journal of Hybrid Imaging 2021 5:14
  2. Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key...

    Authors: Martina Sollini, Francesco Bartoli, Andrea Marciano, Roberta Zanca, Riemer H. J. A. Slart and Paola A. Erba
    Citation: European Journal of Hybrid Imaging 2020 4:24