AI models relying on Google Street View images may misinterpret environmental features, leading to misguided public health efforts to reduce obesity and diabetes, a new study warns. Study: Utilizing big data without domain knowledge impacts public health decision-making . Image Credit: TippaPat / Shutterstock.
com A recent study published in the journal PNAS Environmental Sciences reveals that relying on artificial intelligence (AI) and Google Street View (GSV) images to support urban planning may lead to misleading conclusions that could have detrimental effects on public health interventions aimed at combating obesity and diabetes. How is AI used in urban planning? Recent advancements in AI have accelerated the incorporation of this technology into crucial fields, such as public health and urban planning, which can potentially affect large numbers of people at the community level. For example, GSV images have been combined with object detection by deep learning to evaluate the health outcomes associated with neighborhood properties defined by census tract.
GSV data provides information about the environment, including the types of vegetation, as well as urban development, such as road networks and building structures. These data have been mined using deep learning to devise local interventions targeting mental and cardiometabolic disease and the prevalence of the coronavirus disease 2019 (COVID-19). The study analyzed over 2 million Google Street View images in New York City.
