AI models can be trained to predict outcomes in meningioma patients, according to new research published in npjDigital Medicine. The study’s authors even developed a free smartphone app so others can explore their work.
AI promises to make a titanic impact on radiology, but most of the attention tends to focus on its ability to identify important findings in medical images. What about the technology’s non-interpretive qualities?
Machine learning-based algorithms can predict how patients will respond to antidepressants, according to new research published in Nature Biotechnology. The secret, the authors revealed, is electroencephalography (EEG) data.
AI models can predict when patients may be at an increased risk of in-hospital mortality, according to new research published in JAMA Network Open. If implemented, such models could be used to help healthcare providers improve decision-making and deliver better patient care.
Machine learning (ML) can provide significant value in the field of palliative care. However, researchers still have a lot of unexplored ground to cover before the technology reaches its full potential.
Chun Yuan, PhD, has received a two-year, $200,000 grant from the American Heart Association’s Institute for Precision Cardiovascular Medicine for his work on using AI to detect blocked arteries and cardiovascular risk.