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.

Ultra-flat, featherlight, and compact – these are the hallmarks of the next-generation cooling units being developed by Professor Paul Motzki at Saarland University. Backed by a prestigious ERC Starting Grant from the European Research Council, Motzki is pushing the boundaries of elastocaloric technology – a cutting-edge, climate-friendly method of cooling and heating – by combining two breakthrough smart material systems. This marks the third major success for Paul Motzki in just two years in the EU’s Horizon Europe research and innovation programme. He has already secured an EIC Pathfinder grant for visionary technologies and is also involved in a multimillion-euro EU cluster project.

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of robotic arms to work together faster and smarter in busy industrial settings, potentially saving manufacturers hundreds of hours of planning time and unlocking new levels of flexibility and efficiency.