Artificial Intelligence Creates New Possibilities in Radiology

(April 5, 2018) – The medical use of artificial intelligence (AI) applications, including machine learning and deep learning, is growing at an astounding rate. Applications of the technology in research, radiology workflow and education are increasing exponentially.

The Radiological Society of North America (RSNA) recognizes the value this technology brings to diagnosis and treatment of patients and has launched several research and education initiatives surrounding AI in radiology.

There was a huge interest at AI-related events at RSNA 2017, and there will continue to be a focus on the topic at the upcoming RSNA annual meeting—including an abundance of research, dedicated education areas, and the Machine Learning Showcase on the exhibit floor. In addition, RSNA is cosponsoring a National Institute of Biomedical Imaging and Bioengineering (NIBIB) invitational consensus workshop on artificial intelligence in medical imaging to be held in late summer 2018, and is launching a new online journal, Radiology: Artificial Intelligence, in 2019.

"Radiologists will play a key role in developing AI and making sure the final products meet our needs and the needs of our patients and referring clinicians," said Elliot K. Fishman, M.D., professor of radiology and oncology at the Johns Hopkins University School of Medicine in Baltimore, Md.

While there has been a lot of hype, and even trepidation, about the role AI will ultimately play in radiology, the reality is that AI technologies will add value to the specialty in a number of ways.

"Deep learning is not going to replace us," said Paul Chang, M.D., of the University of the Chicago School of Medicine, during an RSNA 2017 session. "But it will redefine us."

Radiology will need this technology more than ever due to the increasing demands on clinical imaging. Data sets are getting more complex, and there is an increasing need to correlate images with other clinical information in order to implement practices such as radiogenomics.

"Deep learning will help us to meet these new imaging challenges," Dr. Chang said.

Signs of the feverish activity in the AI-radiology arena are everywhere. For example, 2017 saw FDA approval of a self-teaching artificial neural network to help physicians diagnose heart problems and software that provides AI-assisted stroke detection for CT scans and automatically notifies neurovascular specialists by text message. A new smartphone app uses algorithms based on millions of stored images to calculate the likelihood that a mole is becoming cancerous.

AI has also made significant contributions in research of serious medical conditions like Alzheimer's disease, breast cancer, tuberculosis, and pulmonary hypertension.

Leading radiology experts suggest that AI will invigorate the profession and make radiologists more vital than ever to patient care.

"One reason radiology has been so successful is that we are constantly changing," Dr. Fishman said. "If anything, deep learning will guarantee our survival."

Feel free to browse the selection of research and education articles on artificial intelligence at the links below and contact the RSNA media relations team for interviews with leading experts on this exciting topic in radiology.

Images are available for media use. Contact RSNA media relations for assistance.

AI Research

Radiology: "High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision"

Radiology: "Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks"

Radiology: "Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study"

Radiology: "Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease"

RSNA News: "Machine Learning Aids Pulmonary Hypertension"

RSNA Newsroom: "Machine Learning Identifies Breast Lesions Likely to Become Cancer"

RSNA Newsroom: "New Studies Show Brain Impact of Youth Football"

RSNA Newsroom: "Artificial Intelligence May Help Diagnose Tuberculosis in Remote Areas"

RSNA Newsroom: "Artificial Intelligence May Aid in Alzheimer's Diagnosis"

AI Reviews and Commentary

Radiology: Machine Learning Applied to Alzheimer Disease

Radiology: "When Machines Think: Radiology's Next Frontier"

RSNA News: "The Reality of Deep Learning/Artificial Intelligence in Radiology: They Will Redefine the Specialty"

RSNA News: "Machine Learning is the Next Chapter of Radiology, Not the Last"

AI Background

RadioGraphics: "Machine Learning for Medical Imaging"

RadioGraphics: "Deep Learning: A Primer for Radiologists"

RadioGraphics: "Artificial Intelligence in Radiology: Decision Support Systems"

RadioGraphics: "An Artificial Intelligence Program for the Radiologic Diagnosis of Brain Lesions"