That's according to a Jan. 17 white paper published by the American Society of Radiologic Technologists.
Experts believe their approach will allow specialists to pinpoint brain-related pathologies—such as physical injuries, cancer or language disorders, among other things—with improved accuracy.
This most recent approval marks the fourth of its kind for Tel Aviv, Israel-based Aidoc.
AI trained and tested on more than 8,000 biopsies was nearly perfect at spotting differences in samples with or without cancer.
By combining AI with coronary artery calcium scoring and other cardiac measurements, the team would have prevented 73 unnecessary scans.
The new approach can diagnose brain tumors similarly to humans, but in a fraction of the time.
The Silicon Valley company's DeepMind AI beat out six expert readers and with further clinical testing could change the face of early breast cancer detection.
It’s no surprise that AI dominated the landscape this past year, but there were still a number of important stories that will likely become trending topics as radiology continues to evolve.
Radiomic analysis can extract mounds of information from MRIs and help researchers determine if a patient’s cancer is likely to return 10 years after treatment.
Machine learning is more accurate at predicting the long-term risk of potentially life-threatening cardiac events compared to standard clinical assessments, and eventually may revolutionize cardiovascular care.
The deep learning-based model yielded a lower false-negative rate for more aggressive cancers compared to traditional approaches.
AI holds tremendous promise for making radiologists more efficient, but when it comes to cancer care, a few experts believe the coming tech revolution may encounter a few problems.