An AI-optimized American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) improved risk stratification of thyroid nodules and may be easier for readers to use, according to a new study published in Radiology.

The deep learning platform, tested on more than 6,000 cases from the National Lung Cancer Screening Trial and Northwestern University, performed similarly to six radiologists.

The method included three deep convolutional neural networks which outperformed five clinical radiologists, according to results of a study published in Radiology: Artificial Intelligence.

“The value of AI applications in medical care can only be confirmed when professional guidelines provide recommendations for their use in specific clinical settings and patient populations,” wrote Gerold Porenta, MD, PhD, in a recent commentary published in the Journal of Nuclear Medicine.

A machine learning algorithm trained to read imaging scans was more accurate at predicting heart attacks or death than expertly trained physicians, according to a study presented at the International Conference on Nuclear Cardiology and Cardiac CT (ICNC) in Lisbon, Portugal, on May 12.

The deep learning (DL) model was also equally as accurate for racial minorities who have proven to be more likely to die from cancer, such as black women, according to a May 7 study published in Radiology.


“The results of this study demonstrate proof of the concept that DL models may aid in the prediction of thrombolysis outcomes,” wrote authors of an April 30 study published in Academic Radiology.

“Our research demonstrates that deep-learning models integrating routine imaging scans obtained at multiple time points can improve predictions of survival and cancer-specific outcomes for lung cancer," wrote Hugo Aerts, PhD, in a recent study published in Clinical Cancer Research.

The supply of radiation oncologists hasn’t kept up with the global demand for radiation therapy. But could experts from across the world help create an AI algorithm capable of closing that gap?

The human-level success of deep learning has made some in medicine question whether automation may eventually take over many tasks performed by radiologists. An author, and radiologist, put that question to bed in an April 18 editorial published in the Journal of the American College of Radiology.

“Our goal was to provide a blueprint for professional societies, funding agencies, research labs, and everyone else working in the field to accelerate research toward AI innovations that benefit patients," wrote lead author, Curtis P. Langlotz, MD, PhD.

A recent NPR report traced the development and approval of the fist AI software approved to diagnose diabetic retinopathy and examined challenges the administration may face as more software makers look to enter the market.