A new study explains why smokers have a higher chance of developing pancreatic cancer and why they tend to have worse outcomes than nonsmokers.

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a critical issue that can affect diagnostic accuracy and treatment decisions. The findings were published in the September 4 online issue of the Journal of Medical Internet Research [DOI: 10.2196/71757]. To tackle the problem, the investigators developed AEquity, a tool that helps detect and correct bias in health care datasets before they are used to train artificial intelligence (AI) and machine-learning models. The investigators tested AEquity on different types of health data, including medical images, patient records, and a major public health survey, the National Health and Nutrition Examination Survey, using a variety of machine-learning models. The tool was able to spot both well-known and previously overlooked biases across these datasets.

Complement, a component of the innate immune system, has been pinpointed by a research team at the Medical University of South Carolina as playing a key role in inflammatory responses that contribute to fetal neural inflammation and preterm birth, the latter of which represents the primary driver of complications and death in newborns. The team reports its findings in Cells.