Artificial Intelligence Adds Spectrum of Value to Radiology
At RSNA 2018, leading experts discussed the spectrum of capabilities for AI that continues to permeate every area of health care
Artificial Intelligence Will Enhance the Humanity of Health Care
BY JENNIFER ALLYN
Ambient Intelligence Could be Infused Hospital-Wide
Eventually, Dr. Li believes that AI can get to a place where it can detect and reason the type of actions taking place and predict the activity that should take place next. For example, in senior care, Dr. Li noted the importance of patient mobility and understanding how a patient is moving or, in the case of a fall, not moving.
Experts Share the Latest Findings on Informatics Research
BY RICHARD DARGAN
Radiology Study on Training Algorithms
New research reached eye-opening conclusions about the optimal number of images needed to train an algorithm. A November 2018 study in Radiology that looked at the automated classification of chest radiographs found that the DL model’s accuracy improved significantly when the number of images used to train the algorithm jumped from 2,000 to 20,000. However, accuracy improved only marginally when the number of training images increased from 20,000 to 200,000.
“That’s actually a useful thing, that maybe we don’t need to have millions of images in order to train the system,” Dr. Kahn said. “Maybe having a modest number would be a good start, along with other approaches that you could perhaps superimpose on top of that.”
As for mining the data itself, Dr. Kahn pointed to Natural Language Processing (NLP) as a promising avenue of research. NLP is the overarching term used to describe the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written input.
“NLP is using various systems to help mine data out of electronic health records,” he said. “Most of the information in electronic health records is text, and a lot of the resultant information is in the form of narrative text.”