Charles Kahn, Jr., MD, editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, explains the work involved integrating AI in radiology systems and the role of AI in augmenting patient care.
 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.  

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

Validation and testing of all algorithms is needed to eliminate any biases in the data used to train the AI, according to Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America.

The ambitious project seeks to exponentially increase new data within the U.K. Biobank, a prominent large-scale biomedical database and research resource. 

The acquisition represents a significant advance in interoperability and will combine networks that total over 80 billion images globally, including data from institutions such as Mayo Clinic, Cleveland Clinic and New York Presbyterian Hospital.

With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging.