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.
AI can help improve malaria screening in low-resource settings, according to a new study published in the Journal of Digital Imaging. The model developed by researchers is as precise as human experts—and “several orders of magnitude” faster.