Using artificial intelligence to understand lung and bronchus cancer mortality rates

Many people think of robots when they hear the term "artificial intelligence (AI)." However, in the case of a new study on lung and bronchus cancer (LBC) in the U.S., AI refers to various machine learning models stacked together to make high-level predictions about LBC mortality rates. University at Buffalo researchers Zia U. Ahmed, Kang Sun, Michael Shelly and Lina Mu authored the new study, which identifies key risk factors of LBC mortality using explainable artificial intelligence, or XAI. While smoking prevalence, poverty and a community's elevation were most important in predicting LBC mortality rates among the risk factors studied, associations between risk factors and LBC mortality rates were found to vary spatially, and the research explored these geographic differences. The paper, "Explainable artificial intelligence for exploring spatial variability of lung and bronchus cancer mortality rates in the contiguous U.S.," was published in the journal Scientific Reports in December 2021.