The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being developed by researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH). Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined. "It's the biggest cancer killer because it's relatively common and relatively hard to treat, especially once it has reached an advanced stage," says Florian Fintelmann, MGCC thoracic interventional radiologist and coauthor on the new work. "In this case, it's important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it's advanced, the five-year survival rate is just short of 10 percent."

In literature, characters that smoke are often described as haggard and older looking, with facial features that are associated with worn leather. While these depictions arguably carry over into reality, what is for certain is that the association between smoking, cancer, and cardiovascular disease is strong. Unfortunately, however, the connection between smoking and biological aging has been less clear. Yet, a new study from an international team of investigators led by scientists at Insilico Medicine may change how smoking is evaluated at the biochemical level. "In this study, we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results and the recent advances in artificial intelligence (AI)," the study authors explained.