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.

More than 6.1 million children were diagnosed with ADHD in 2016. Despite these numbers, there is no single test or imaging exam that can confidently diagnose a patient.

The tool, detailed in an EBioMedicine study published last month, can sift through the multitudes of cells in a tissue sample and identify tumors’ growth patterns, along with other highly useful information for predicting health outcomes.

The public workshop will take place this upcoming February and will discuss computer-aided detection and diagnosis software, computer-aided triage systems and image quality enhancement algorithms, among other topics.

The technique combines AI with patient-specific health and cost information for a rough estimate on an individual's five-year healthcare expenditures.