Assistance from an artificial intelligence (AI) algorithm with high diagnostic accuracy improved radiologist performance in detecting lung cancers on chest X-rays and increased human acceptance of AI suggestions, according to a study published in the journal Radiology.
Diagnostic imaging is indispensable in health care, as it allows clinicians to detect and diagnose a variety of medical conditions. Despite significant advances to imaging technology, however, existing single imaging techniques often fall short of addressing all diagnostic scenarios, which leads to an increased reliance on multiple imaging types and higher health care costs.
Researchers have found that digital breast tomosynthesis (DBT) has improved breast cancer screening performance in community practice and identifies more invasive cancers, compared to digital mammography.
Skoltech researchers have trained a neural network to search for lung pathologies on X-ray images and come up with brief verbal descriptions to accompany them. This task is currently performed by physicians, and it takes several minutes to complete.
According to study published in the American Journal of Roentgenology (AJR), MRI-based surveillance after surgical treatment of musculoskeletal soft-tissue sarcomas (STS) shows utility for detecting clinically occult local recurrences (LRs), potentially improving patient outcomes.
