Described in the journal Nature Communications, the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead – something that has eluded scientists for decades. Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below, the researchers said. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain, they said. These accelerating changes, the researchers noted, have dramatic consequences for the world climate, for Arctic ecosystems, and Indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle. IceNet is almost 95 per cent accurate in predicting whether sea ice will be present two months ahead – better than the leading physics-based model, according to the researchers.

Cancer is the second leading cause of death worldwide[i], with an estimated 18.1 million new cases and 9.6 million deaths attributed to it in 2018[ii]. The search for more effective anti-cancer drugs is a global effort involving academia and industry. In our Computational Systems Biology group at the IBM Research lab in Zurich, we are building machine learning approaches that can potentially help to accelerate our understanding of the leading drivers and molecular mechanisms of these complex diseases, as well as the differences in tumor composition occurring across various cancer types. Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies. At the 18th European Conference on Computational Biology (ECCB) and the 27th Conference on Intelligent Systems for Molecular Biology (ISMB) to be held from July 21 -25 in Basel, Switzerland, IBM will present significant, novel research that led to the implementation of three machine learning solutions aimed at accelerating and guiding cancer research.

A novel artificial intelligence or AI tool, developed by researchers at Stanford University may assist radiologists for fast detection of brain aneurysms revealed the findings of a study published JAMA Network Open. The paper highlighted areas of a brain scan that are likely to contain an aneurysm. A brain aneurysm is characterized as bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damage or death. This tool, which is built around an algorithm called HeadXNet, improved clinicians' ability to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians. While the success of HeadXNet in these experiments is promising, the team of researchers – who have expertise in machine learning, radiology, and neurosurgery – cautions that further investigation is needed to evaluate the generalizability of the AI tool prior to real-time clinical deployment given differences in scanner hardware and imaging protocols across different hospital centers.