An artificial intelligence (AI) tool—trained on roughly a million screening mammography images—identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds.
“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI”— Krzysztof J. Geras, PhD, Senior study author
Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer programme, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images.
“Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa,” says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone.
“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI,” adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. “The ultimate goal of our work is to augment, not replace, human radiologists.”
A New Tool to Identify Breast Cancer
Modern AI approaches, inspired by the human brain, use complex circuits to process information in layers, with each step feeding information into the next, and assigning more or less importance to each piece of information along the way.
“The transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars”— Nan Wu, a doctoral candidate at the NYU Center for Data Science
In addition, the researchers designed the study AI model to first consider very small patches of the full resolution image separately to create a heat map, a statistical picture of disease likelihood. Then the programme considers the entire breast for structural features linked to cancer, paying closer attention to the areas flagged in the pixel-level heat map.
“The transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars—slowly and carefully, building trust, and improving systems along the way with a focus on safety,” says first author Nan Wu, a doctoral candidate at the NYU Center for Data Science.